{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T05:45:27Z","timestamp":1784612727932,"version":"3.55.0"},"reference-count":154,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T00:00:00Z","timestamp":1683244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42074039"],"award-info":[{"award-number":["42074039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["SJCX21_0040"],"award-info":[{"award-number":["SJCX21_0040"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Post graduate Research and Practice Innovation Program of Jiangsu Province","award":["42074039"],"award-info":[{"award-number":["42074039"]}]},{"name":"Post graduate Research and Practice Innovation Program of Jiangsu Province","award":["SJCX21_0040"],"award-info":[{"award-number":["SJCX21_0040"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The specific characteristics of remote sensing images, such as large directional variations, large target sizes, and dense target distributions, make target detection a challenging task. To improve the detection performance of models while ensuring real-time detection, this paper proposes a lightweight object detection algorithm based on an attention mechanism and YOLOv5s. Firstly, a depthwise-decoupled head (DD-head) module and spatial pyramid pooling cross-stage partial GSConv (SPPCSPG) module were constructed to replace the coupled head and the spatial pyramid pooling-fast (SPPF) module of YOLOv5s. A shuffle attention (SA) mechanism was introduced in the head structure to enhance spatial attention and reconstruct channel attention. A content-aware reassembly of features (CARAFE) module was introduced in the up-sampling operation to reassemble feature points with similar semantic information. In the neck structure, a GSConv module was introduced to maintain detection accuracy while reducing the number of parameters. Experimental results on remote sensing datasets, RSOD and DIOR, showed an improvement of 1.4% and 1.2% in mean average precision accuracy compared with the original YOLOv5s algorithm. Moreover, the algorithm was also tested on conventional object detection datasets, PASCAL VOC and MS COCO, which showed an improvement of 1.4% and 3.1% in mean average precision accuracy. Therefore, the experiments showed that the constructed algorithm not only outperformed the original network on remote sensing images but also performed better than the original network on conventional object detection images.<\/jats:p>","DOI":"10.3390\/rs15092429","type":"journal-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T08:12:11Z","timestamp":1683274331000},"page":"2429","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["A Lightweight Object Detection Algorithm for Remote Sensing Images Based on Attention Mechanism and YOLOv5s"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3545-5032","authenticated-orcid":false,"given":"Pengfei","family":"Liu","sequence":"first","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Mi","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youchen","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Haq, M.A., Ahmed, A., Khan, I., Gyani, J., Mohamed, A., Attia, E., Mangan, P., and Pandi, D. (2022). Analysis of environmental factors using AI and ML methods. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-16665-7"},{"key":"ref_2","first-page":"4599","article-title":"Deep Learning Based Modeling of Groundwater Storage Change","volume":"70","author":"Haq","year":"2022","journal-title":"CMC Comput. Mat. Contin."},{"key":"ref_3","first-page":"2363","article-title":"CDLSTM: A Novel Model for Climate Change Forecasting","volume":"71","author":"Haq","year":"2022","journal-title":"CMC Comput. Mat. Contin."},{"key":"ref_4","first-page":"1403","article-title":"SMOTEDNN: A Novel Model for Air Pollution Forecasting and AQI Classification","volume":"71","author":"Haq","year":"2022","journal-title":"CMC Comput. Mat. Contin."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ning, Z., Sun, S., Wang, X., Guo, L., Wang, G., Gao, X., and Kwok, R.Y.K. (2021). Intelligent resource allocation in mobile blockchain for privacy and security transactions: A deep reinforcement learning based approach. Sci. China Inf. Sci., 64.","DOI":"10.1007\/s11432-020-3125-y"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wang, H., Liu, X., He, H.R., Gu, Q., and Sun, W. (2019). Learning to See the Hidden Part of the Vehicle in the Autopilot Scene. Electronics, 8.","DOI":"10.3390\/electronics8030331"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., Pradhan, B., Shukla, N., Chakraborty, S., and Alamri, A. (2020). Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review. Remote Sens., 12.","DOI":"10.3390\/rs12091444"},{"key":"ref_8","first-page":"135","article-title":"Survey of Road Extraction Methods in Remote Sensing Images Based on Deep Learning","volume":"90","author":"Liu","year":"2022","journal-title":"PFG\u2014J. Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8939","DOI":"10.1007\/s11042-022-11954-9","article-title":"Detection of cervical cancer cells in complex situation based on improved YOLOv3 network","volume":"81","author":"Jia","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shaheen, H., Ravikumar, K., Lakshmipathi Anantha, N., Uma Shankar Kumar, A., Jayapandian, N., and Kirubakaran, S. (2023). An efficient classification of cirrhosis liver disease using hybrid convolutional neural network-capsule network. Biomed. Signal. Process. Control., 80.","DOI":"10.1016\/j.bspc.2022.104152"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s13007-022-00866-2","article-title":"A survey of few-shot learning in smart agriculture: Developments, applications, and challenges","volume":"18","author":"Yang","year":"2022","journal-title":"Plant Methods"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4281","DOI":"10.1109\/TITS.2020.2980864","article-title":"Solving the Security Problem of Intelligent Transportation System with Deep Learning","volume":"22","author":"Lv","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2489","DOI":"10.1007\/s11042-021-11649-7","article-title":"A review of hashing based image authentication techniques","volume":"81","author":"Shaik","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, H., Xue, X., Jiang, Y., and Shen, Q. (2018). Deep learning for remote sensing image classification: A survey. WIREs Data Min. Knowl. Discov., 8.","DOI":"10.1002\/widm.1264"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6024","DOI":"10.1109\/TPAMI.2021.3085766","article-title":"Concealed Object Detection","volume":"44","author":"Fan","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","first-page":"3523","article-title":"Image Segmentation Using Deep Learning: A Survey","volume":"44","author":"Minaee","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1080\/13658816.2017.1324976","article-title":"Classifying urban land use by integrating remote sensing and social media data","volume":"31","author":"Liu","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, R., Tao, F., Liu, X., Na, J., Leng, H., Wu, J., and Zhou, T. (2022). RAANet: A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14133109"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, S., Lyu, D., Huang, G., Zhang, X., Gao, F., Chen, Y., and Liu, X. (2020). Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China. J. Transp. Geogr., 82.","DOI":"10.1016\/j.jtrangeo.2019.102631"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hu, S., Fong, S., Yang, L., Yang, S., Dey, N., Millham, R.C., and Fiaidhi, J. (2021). Fast and Accurate Terrain Image Classification for ASTER Remote Sensing by Data Stream Mining and Evolutionary-EAC Instance-Learning-Based Algorithm. Remote Sens., 13.","DOI":"10.3390\/rs13061123"},{"key":"ref_21","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tang, X., Zhou, P., and Wang, P. (2016, January 27\u201329). Real-time image-based driver fatigue detection and monitoring system for monitoring driver vigilance. Proceedings of the 2016 35th Chinese Control Conference (CCC), Chengdu, China.","DOI":"10.1109\/ChiCC.2016.7554007"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1109\/TPAMI.2012.28","article-title":"Measuring the Objectness of Image Windows","volume":"34","author":"Alexe","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1218","DOI":"10.1109\/JBHI.2017.2731873","article-title":"Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks","volume":"22","author":"Yap","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kong, T., Yao, A., Chen, Y., and Sun, F. (2016, January 27\u201330). HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.98"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cho, M., Chung, T.Y., Lee, H., and Lee, S. (2019, January 22\u201325). N-RPN: Hard Example Learning for Region Proposal Networks. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803519"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"176158","DOI":"10.1109\/ACCESS.2020.3022351","article-title":"FPSiamRPN: Feature Pyramid Siamese Network with Region Proposal Network for Target Tracking","volume":"8","author":"Rao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.neucom.2017.12.070","article-title":"Cascade region proposal and global context for deep object detection","volume":"395","author":"Zhong","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3442","DOI":"10.1109\/TIP.2019.2960869","article-title":"End-to-End Optimized ROI Image Compression","volume":"29","author":"Cai","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shaik, A.S., Karsh, R.K., Islam, M., Singh, S.P., and Wan, S. (2022). A Secure and Robust Autoencoder-Based Perceptual Image Hashing for Image Authentication. Wirel. Commun. Mob. Comput., 2022.","DOI":"10.1155\/2022\/1645658"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Seferbekov, S., Iglovikov, V., Buslaev, A., and Shvets, A. (2018, January 18\u201322). Feature Pyramid Network for Multi-class Land Segmentation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00051"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). Computer Vision\u2014ECCV 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46478-7"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_37","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_38","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, C., Liao, H.M., Wu, Y., Chen, P., Hsieh, J., and Yeh, I. (2020, January 13\u201319). CSPNet: A New Backbone that can Enhance Learning Capability of CNN. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_43","unstructured":"Li, H., Xiong, P., An, J., and Wang, L. (2018). Pyramid Attention Network for Semantic Segmentation. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2070","DOI":"10.1109\/LGRS.2014.2319082","article-title":"Automatic Detection of Inshore Ships in High-Resolution Remote Sensing Images Using Robust Invariant Generalized Hough Transform","volume":"11","author":"Xu","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","unstructured":"Cucchiara, R., Grana, C., Piccardi, M., Prati, A., and Sirotti, S. (2001, January 25\u201329). Improving shadow suppression in moving object detection with HSV color information. Proceedings of the ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585), Oakland, CA, USA."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5837","DOI":"10.1080\/01431161.2010.512310","article-title":"A complete processing chain for ship detection using optical satellite imagery","volume":"31","author":"Corbane","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1109\/TIP.2010.2099128","article-title":"Saliency and Gist Features for Target Detection in Satellite Images","volume":"20","author":"Li","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2004.11.015","article-title":"Oil spill detection by satellite remote sensing","volume":"95","author":"Brekke","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.isprsjprs.2013.08.001","article-title":"Object detection in remote sensing imagery using a discriminatively trained mixture model","volume":"85","author":"Cheng","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.patrec.2005.08.013","article-title":"Car detection in aerial thermal images by local and global evidence accumulation","volume":"27","author":"Hinz","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5512","DOI":"10.1109\/TGRS.2019.2899955","article-title":"R2-CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images","volume":"57","author":"Pang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Fu, Y., Wu, F., and Zhao, J. (2018, January 20\u201324). Context-Aware and Depthwise-based Detection on Orbit for Remote Sensing Image. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8545815"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images","volume":"54","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1109\/TIP.2018.2867198","article-title":"Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection","volume":"28","author":"Cheng","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"50839","DOI":"10.1109\/ACCESS.2018.2869884","article-title":"Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network","volume":"6","author":"Yang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, W., Wang, S., Thachan, S., Chen, J., and Qian, Y. (2018, January 22\u201327). Deconv R-CNN for Small Object Detection on Remote Sensing Images. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517436"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, L., Cheng, L., Guo, X., Liu, X., Jiao, L., and Liu, F. (October, January 26). Deep Adaptive Proposal Network in Optical Remote Sensing Images Objective Detection. Proceedings of the IGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324275"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Guo, W., Yang, W., Zhang, H., and Hua, G. (2018). Geospatial Object Detection in High Resolution Satellite Images Based on Multi-Scale Convolutional Neural Network. Remote Sens., 10.","DOI":"10.3390\/rs10010131"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhu, K., Chen, G., Tan, X., Zhang, L., Dai, F., Liao, P., and Gong, Y. (2019). Geospatial Object Detection on High Resolution Remote Sensing Imagery Based on Double Multi-Scale Feature Pyramid Network. Remote Sens., 11.","DOI":"10.3390\/rs11070755"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1109\/TGRS.2017.2778300","article-title":"Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Li, Q., Mou, L., Jiang, K., Liu, Q., Wang, Y., and Zhu, X. (2018, January 22\u201327). Hierarchical Region Based Convolution Neural Network for Multiscale Object Detection in Remote Sensing Images. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518345"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","article-title":"Attention mechanisms in computer vision: A survey","volume":"8","author":"Guo","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018). Computer Vision\u2014ECCV 2018, Springer International Publishing.","DOI":"10.1007\/978-3-030-01252-6"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Hao, Z., Wang, Z., Bai, D., Tao, B., Tong, X., and Chen, B. (2022). Intelligent Detection of Steel Defects Based on Improved Split Attention Networks. Front. Bioeng. Biotechnol., 9.","DOI":"10.3389\/fbioe.2021.810876"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual Attention Network for Scene Segmentation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_66","unstructured":"Guan, Q., Huang, Y., Zhong, Z., Zheng, Z., Zheng, L., and Yang, Y. (2018). Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification. arXiv."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Dai, T., Cai, J., Zhang, Y., Xia, S.T., and Zhang, L. (2019, January 15\u201320). Second-Order Attention Network for Single Image Super-Resolution. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01132"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., and He, X. (2018, January 18\u201322). AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00143"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Kumar, A., Senatore, S., and Gunjan, V.K. (2022). ICDSMLA 2020, Springer.","DOI":"10.1007\/978-981-16-3690-5"},{"key":"ref_71","unstructured":"Mnih, V., Heess, N., Graves, A., and Kavukcuoglu, K. (2014, January 8\u201313). Recurrent Models of Visual Attention. Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_72","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., and Kavukcuoglu, K. (2015, January 7\u201312). Spatial Transformer Networks. Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_73","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., and Vedaldi, A. (2018, January 3\u20138). Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks. Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., and Liu, W. (November, January 27). CCNet: Criss-Cross Attention for Semantic Segmentation. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00069"},{"key":"ref_75","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. arXiv."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Li, J., Zhang, S., Wang, J., Gao, W., and Tian, Q. (November, January 27). Global-Local Temporal Representations for Video Person Re-Identification. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00406"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wang, L., Wu, W., Qian, C., and Lu, T. (2021, January 10\u201317). TAM: Temporal Adaptive Module for Video Recognition. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01345"},{"key":"ref_78","unstructured":"Srivastava, R.K., Greff, K., and Schmidhuber, J.U.R. (2015, January 7\u201312). Training Very Deep Networks. Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., and Yang, J. (2019, January 15\u201320). Selective Kernel Networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref_80","unstructured":"Yang, B., Bender, G., Le, Q.V., and Ngiam, J. (2019, January 8\u201314). CondConv: Conditionally Parameterized Convolutions for Efficient Inference. Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., and Liu, Z. (2020, January 13\u201319). Dynamic Convolution: Attention Over Convolution Kernels. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., and Chua, T.S. (2017, January 21\u201326). SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.667"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., and Tang, X. (2017, January 21\u201326). Residual Attention Network for Image Classification. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Song, S., Lan, C., Xing, J., Zeng, W., and Liu, J. (2017, January 4\u20139). An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11212"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1049\/iet-its.2016.0208","article-title":"LSTM network: A deep learning approach for short-term traffic forecast","volume":"11","author":"Zhao","year":"2017","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Wang, L., Ouyang, W., Wang, X., and Lu, H. (2015, January 7\u201313). Visual Tracking with Fully Convolutional Networks. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.357"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lin, T.Y., and Le, Q.V. (2019, January 15\u201320). NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00720"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1007\/s12524-020-01265-7","article-title":"Spatial\u2013Spectral Image Classification with Edge Preserving Method","volume":"49","author":"Merugu","year":"2021","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_91","unstructured":"Liu, S., Di, H., and Wang, Y. (2019). Learning Spatial Fusion for Single-Shot Object Detection. arXiv."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, W., Li, Z., Shu, S., Lang, X., Zhang, T., and Dong, J. (2023). Development of a cross-scale weighted feature fusion network for hot-rolled steel surface defect detection. Eng. Appl. Artif. Intell., 117.","DOI":"10.1016\/j.engappai.2022.105628"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1080\/17538947.2022.2163514","article-title":"Bridge detection method for HSRRSIs based on YOLOv5 with a decoupled head","volume":"16","author":"Qiu","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_94","unstructured":"Liang, M., Liu, X., and Hu, X. (2023). Small target detection algorithm for train operating environment image based on improved YOLOv3. J. Comput. Appl., 1\u201312."},{"key":"ref_95","first-page":"2675","article-title":"An Algorithm for Detecting Prohibited Items in X-ray Images Based on Improved YOLOv5","volume":"42","author":"Li","year":"2023","journal-title":"Comput. Eng. Appl."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"11463","DOI":"10.1109\/ACCESS.2023.3241630","article-title":"CRAS-YOLO: A Novel Multi-Category Vessel Detection and Classification Model Based on YOLOv5s Algorithm","volume":"11","author":"Zhao","year":"2023","journal-title":"IEEE Access"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Luo, X., Wu, Y., and Zhao, L. (2022). YOLOD: A Target Detection Method for UAV Aerial Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14143240"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Chun, S., Oh, S.J., Yoo, Y., and Choe, J. (November, January 27). CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV). IEEE: Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Ran, X., Zhou, X., Lei, M., Tepsan, W., and Deng, W. (2021). A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots. Appl. Sci., 11.","DOI":"10.3390\/app112311202"},{"key":"ref_100","first-page":"100","article-title":"Yield estimation method of apple tree based on improved lightweight YOLOv5","volume":"3","author":"Li","year":"2021","journal-title":"Smart Agric."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"8574","DOI":"10.1109\/TCYB.2021.3095305","article-title":"Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation","volume":"52","author":"Zheng","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_102","unstructured":"Li, X., Hu, X., and Yang, J. (2019). Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks. arXiv."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Zhang, Q., and Yang, Y. (2021, January 6\u201311). SA-Net: Shuffle Attention for Deep Convolutional Neural Networks. Proceedings of the ICASSP 2021\u20142021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Song, G., Liu, Y., and Wang, X. (2020, January 13\u201319). Revisiting the Sibling Head in Object Detector. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01158"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Wu, Y., Chen, Y., Yuan, L., Liu, Z., Wang, L., Li, H., and Fu, Y. (2020, January 13\u201319). Rethinking Classification and Localization for Object Detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01020"},{"key":"ref_106","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). YOLOX: Exceeding YOLO Series in 2021. arXiv."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"3396","DOI":"10.1109\/TGRS.2020.3008286","article-title":"Multiscale Residual Network with Mixed Depthwise Convolution for Hyperspectral Image Classification","volume":"59","author":"Gao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, K., Xu, R., Liu, Z., Loy, C.C., and Lin, D. (November, January 27). CARAFE: Content-Aware ReAssembly of FEatures. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00310"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Zhang, M., Gao, F., Yang, W., and Zhang, H. (2023). Wildlife Object Detection Method Applying Segmentation Gradient Flow and Feature Dimensionality Reduction. Electronics, 12.","DOI":"10.3390\/electronics12020377"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Yang, Z., Li, L., Luo, W., and Ning, X. (2022). PDNet: Improved YOLOv5 Nondeformable Disease Detection Network for Asphalt Pavement. Comput. Intell. Neurosci., 2022.","DOI":"10.1155\/2022\/5133543"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Wu, F., Duan, J., Ai, P., Chen, Z., Yang, Z., and Zou, X. (2022). Rachis detection and three-dimensional localization of cut off point for vision-based banana robot. Comput. Electron. Agric., 198.","DOI":"10.1016\/j.compag.2022.107079"},{"key":"ref_112","unstructured":"Wang, C., Mark, A.B., and Liao, M.H. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"2486","DOI":"10.1109\/TGRS.2016.2645610","article-title":"Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks","volume":"55","author":"Long","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1080\/01431161.2014.999881","article-title":"Elliptic Fourier transformation-based histograms of oriented gradients for rotationally invariant object detection in remote-sensing images","volume":"36","author":"Xiao","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","article-title":"Object detection in optical remote sensing images: A survey and a new benchmark","volume":"159","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The Pascal Visual Object Classes Challenge: A Retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). Computer Vision\u2014ECCV 2014, Springer International Publishing.","DOI":"10.1007\/978-3-319-10599-4"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.neucom.2020.06.011","article-title":"Novel up-scale feature aggregation for object detection in aerial images","volume":"411","author":"Lin","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"2148","DOI":"10.1109\/JSTARS.2020.3046482","article-title":"Cross-Layer Attention Network for Small Object Detection in Remote Sensing Imagery","volume":"14","author":"Li","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.11834\/jrs.20210505","article-title":"Optical remote sensing image object detection based on multiresolution feature fusion","volume":"25","author":"Yao","year":"2021","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Liu, Z., Zhu, C., Qi, J., and Zhao, D. (2021). Object Detection in Remote Sensing Images via Multi-Feature Pyramid Network with Receptive Field Block. Remote Sens., 13.","DOI":"10.3390\/rs13050862"},{"key":"ref_123","first-page":"1","article-title":"FSoD-Net: Full-Scale Object Detection from Optical Remote Sensing Imagery","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_124","first-page":"302","article-title":"Remote Sensing Images Target Detection Based on Adjustable Parameter and Receptive field","volume":"50","author":"Liu","year":"2021","journal-title":"Acta Photonica Sin."},{"key":"ref_125","first-page":"1","article-title":"Multiscale Semantic Fusion-Guided Fractal Convolutional Object Detection Network for Optical Remote Sensing Imagery","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"10879","DOI":"10.1007\/s00500-022-07106-8","article-title":"Improved YOLOv5 network method for remote sensing image-based ground objects recognition","volume":"26","author":"Xue","year":"2022","journal-title":"Soft Comput."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Sun, Y., Liu, W., Gao, Y., Hou, X., and Bi, F. (2022). A Dense Feature Pyramid Network for Remote Sensing Object Detection. Appl. Sci., 12.","DOI":"10.3390\/app12104997"},{"key":"ref_128","unstructured":"Liu, H., Zhang, L., Wang, F., and He, R. (2022). Object detection algorithm based on attention mechanism and context information. J. Comput. Appl., 1\u20139."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"2384","DOI":"10.1109\/TPAMI.2022.3166956","article-title":"SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing","volume":"45","author":"Yang","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Chen, W., Han, B., Yang, Z., and Gao, X. (2022). MSSDet: Multi-Scale Ship-Detection Framework in Optical Remote-Sensing Images and New Benchmark. Remote Sens., 14.","DOI":"10.3390\/rs14215460"},{"key":"ref_131","first-page":"638","article-title":"Object Detection in Remote Sensing Images by Fusing Multi-neuron Sparse Features and Hierarchical Depth Features","volume":"25","author":"Gao","year":"2023","journal-title":"J. Geo Inf. Sci."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Chen, J., Hong, H., Song, B., Guo, J., Chen, C., and Xu, J. (2023). MDCT: Multi-Kernel Dilated Convolution and Transformer for One-Stage Object Detection of Remote Sensing Images. Remote Sens., 15.","DOI":"10.3390\/rs15020371"},{"key":"ref_133","first-page":"2096","article-title":"Attention Based Single Shot Multibox Detector","volume":"43","author":"Zhao","year":"2021","journal-title":"J. Electron. Inf. Technol."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Qu, Z., Han, T., and Yi, T. (2022). MFFAMM: A Small Object Detection with Multi-Scale Feature Fusion and Attention Mechanism Module. Appl. Sci., 12.","DOI":"10.3390\/app12188940"},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Yang, Z., Bu, Z., and Liu, C. (2022). SSD Optimization Model Based on Shallow Feature Fusion. Int. J. Pattern Recognit. Artif. Intell., 36.","DOI":"10.1142\/S0218001422590339"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s11554-023-01258-y","article-title":"FESSD: SSD target detection based on feature fusion and feature enhancement","volume":"20","author":"Qian","year":"2023","journal-title":"J. Real Time Image Process."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Deng, H. (2020). GC-YOLOv3: You Only Look Once with Global Context Block. Electronics, 9.","DOI":"10.3390\/electronics9081235"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Zhang, X., Gao, Y., Wang, H., and Wang, Q. (2020). Improve YOLOv3 using dilated spatial pyramid module for multi-scale object detection. Int. J. Adv. Robot. Syst., 17.","DOI":"10.1177\/1729881420936062"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Zhang, M., Xu, S., Song, W., He, Q., and Wei, Q. (2021). Lightweight Underwater Object Detection Based on YOLO v4 and Multi-Scale Attentional Feature Fusion. Remote Sens., 13.","DOI":"10.3390\/rs13224706"},{"key":"ref_140","unstructured":"He, X., and Song, X. (2023). Improved YOLOv4-Tiny lightweight target detection algorithm. J. Front. Comput. Sci. Technol., 1\u201317."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s13735-022-00229-6","article-title":"PDS-Net: A novel point and depth-wise separable convolution for real-time object detection","volume":"11","author":"Junayed","year":"2022","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Wang, K., Wang, Y., Zhang, S., Tian, Y., and Li, D. (2022). SLMS-SSD: Improving the balance of semantic and spatial information in object detection. Expert Syst. Appl., 206.","DOI":"10.1016\/j.eswa.2022.117682"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Kong, T., Sun, F., Yao, A., Liu, H., Lu, M., and Chen, Y. (2017, January 21\u201326). RON: Reverse Connection with Objectness Prior Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.557"},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Zhou, P., Ni, B., Geng, C., Hu, J., and Xu, Y. (2018, January 18\u201322). Scale-Transferrable Object Detection. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00062"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Qu, Z., Gao, L., Wang, S., Yin, H., and Yi, T. (2022). An improved YOLOv5 method for large objects detection with multi-scale feature cross-layer fusion network. Image Vis. Comput., 125.","DOI":"10.1016\/j.imavis.2022.104518"},{"key":"ref_146","unstructured":"Tu, X., Bao, X., Wu, B., Jin, Y., and Zhang, Q. (2023). Object detection algorithm for 3D coordinate attention path aggregation network. J. Front. Comput. Sci. Technol., 1\u201316."},{"key":"ref_147","first-page":"24","article-title":"A Lightweight Object Detection Algorithm Based on Improved YOLOv5s","volume":"30","author":"Yang","year":"2023","journal-title":"Electron. Opt. Control"},{"key":"ref_148","first-page":"142","article-title":"Improved lightweight YOLOv4 target detection algorithm","volume":"45","author":"Song","year":"2022","journal-title":"Electron. Meas. Technol."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1080\/21642583.2022.2062479","article-title":"SFGNet detecting objects via spatial fine-grained feature and enhanced RPN with spatial context","volume":"10","author":"Hu","year":"2022","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_150","unstructured":"Dai, J.F., Li, Y., He, K.M., and Sun, J. (2016, January 5\u201310). R-FCN: Object Detection via Region-based Fully Convolutional Networks. Proceedings of the 30th Conference on Neural Information Processing Systems (NIPS), Barcelona, Spain."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"104613","DOI":"10.1016\/j.imavis.2022.104613","article-title":"Single stage architecture for improved accuracy real-time object detection on mobile devices","volume":"130","author":"Bacea","year":"2023","journal-title":"Image Vis. Comput."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1049\/cvi2.12072","article-title":"TRC-YOLO: A real-time detection method for lightweight targets based on mobile devices","volume":"16","author":"Wang","year":"2022","journal-title":"IET Comput. Vis."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1049\/ipr2.12340","article-title":"Trident-YOLO: Improving the precision and speed of mobile device object detection","volume":"16","author":"Wang","year":"2022","journal-title":"IET Image Process."},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Xiao, J., Guo, H., Zhou, J., Zhao, T., Yu, Q., Chen, Y., and Wang, Z. (2023). Tiny object detection with context enhancement and feature purification. Expert Syst. Appl., 211.","DOI":"10.1016\/j.eswa.2022.118665"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2429\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:29:50Z","timestamp":1760124590000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2429"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,5]]},"references-count":154,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092429"],"URL":"https:\/\/doi.org\/10.3390\/rs15092429","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,5]]}}}