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of Yuzhong District, Chongqing","award":["cstc2021jcyj-bsh0199"],"award-info":[{"award-number":["cstc2021jcyj-bsh0199"]}]},{"name":"Basic Research and Frontier Exploration Project of Yuzhong District, Chongqing","award":["CSTB2022TIAD-KPX0039"],"award-info":[{"award-number":["CSTB2022TIAD-KPX0039"]}]},{"name":"Basic Research and Frontier Exploration Project of Yuzhong District, Chongqing","award":["20210164"],"award-info":[{"award-number":["20210164"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the rapid advancement of technology, satellite and drone technologies have had significant impacts on various fields, creating both opportunities and challenges. In areas like the military, urban planning, and environmental monitoring, the application of remote sensing technology is paramount. However, due to the unique characteristics of remote sensing images, such as high resolution, large-scale scenes, and small, densely packed targets, remote sensing object detection faces numerous technical challenges. Traditional detection methods are inadequate for effectively detecting small targets, rendering the accurate and efficient detection of objects in complex remote sensing images a pressing issue. Current detection techniques fall short in accurately detecting small targets compared to medium and large ones, primarily due to limited feature information, insufficient contextual data, and poor localization capabilities for small targets. In response, we propose an innovative detection method. Unlike previous approaches that often focused solely on either local or contextual information, we introduce a novel Global and Local Attention Mechanism (GAL), providing an in-depth modeling method for input images. Our method integrates fine-grained local feature analysis with global contextual information processing. The local attention concentrates on details and spatial relationships within local windows, enabling the model to recognize intricate details in complex images. Meanwhile, the global attention addresses the entire image\u2019s global information, capturing overarching patterns and structures, thus enhancing the model\u2019s high-level semantic understanding. Ultimately, a specific mechanism fuses local details with global context, allowing the model to consider both aspects for a more precise and comprehensive interpretation of images. Furthermore, we have developed a multi-head prediction module that leverages semantic information at various scales to capture the multi-scale characteristics of remote sensing targets. Adding decoupled prediction heads aims to improve the accuracy and robustness of target detection. Additionally, we have innovatively designed the Ziou loss function, an advanced loss calculation, to enhance the model\u2019s precision in small target localization, thereby boosting its overall performance in small target detection. Experimental results on the Visdrone2019 and DOTA datasets demonstrate that our method significantly surpasses traditional methods in detecting small targets in remote sensing imagery.<\/jats:p>","DOI":"10.3390\/rs16040644","type":"journal-article","created":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T08:12:03Z","timestamp":1707466323000},"page":"644","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["Remote Sensing Micro-Object Detection under Global and Local Attention Mechanism"],"prefix":"10.3390","volume":"16","author":[{"given":"Yuanyuan","family":"Li","sequence":"first","affiliation":[{"name":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengguo","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9562-3865","authenticated-orcid":false,"given":"Guanqiu","family":"Qi","sequence":"additional","affiliation":[{"name":"Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Hu","sequence":"additional","affiliation":[{"name":"Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqin","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hird, J.N., Montaghi, A., McDermid, G.J., Kariyeva, J., Moorman, B.J., Nielsen, S.E., and McIntosh, A.C. (2017). Use of unmanned aerial vehicles for monitoring recovery of forest vegetation on petroleum well sites. Remote Sens., 9.","DOI":"10.3390\/rs9050413"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kellenberger, B., Volpi, M., and Tuia, D. (2017, January 23\u201328). Fast animal detection in UAV images using convolutional neural networks. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127090"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2017.11.011","article-title":"Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks","volume":"140","author":"Audebert","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_5","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 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_6","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 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103752","DOI":"10.1016\/j.jvcir.2023.103752","article-title":"FE-YOLOv5: Feature enhancement network based on YOLOv5 for small object detection","volume":"90","author":"Wang","year":"2023","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yang, C., Huang, Z., and Wang, N. (2022, January 18\u201324). QueryDet: Cascaded sparse query for accelerating high-resolution small object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01330"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e1145","DOI":"10.7717\/peerj-cs.1145","article-title":"Lightweight multi-scale network for small object detection","volume":"8","author":"Li","year":"2022","journal-title":"PeerJ Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.1109\/TMM.2021.3074273","article-title":"Extended feature pyramid network for small object detection","volume":"24","author":"Deng","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.patrec.2023.03.009","article-title":"Small-object detection based on YOLOv5 in autonomous driving systems","volume":"168","author":"Mahaur","year":"2023","journal-title":"Pattern Recognit. Lett."},{"key":"ref_12","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 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.neunet.2022.08.029","article-title":"Attentional feature pyramid network for small object detection","volume":"155","author":"Min","year":"2022","journal-title":"Neural Netw."},{"key":"ref_14","unstructured":"Yang, X., Yan, J., Ming, Q., Wang, W., Zhang, X., and Tian, Q. (2021, January 18\u201324). Rethinking rotated object detection with gaussian wasserstein distance loss. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_15","first-page":"18381","article-title":"Learning high-precision bounding box for rotated object detection via kullback-leibler divergence","volume":"34","author":"Yang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Qi, G., Zhang, Y., Wang, K., Mazur, N., Liu, Y., and Malaviya, D. (2022). Small object detection method based on adaptive spatial parallel convolution and fast multi-scale fusion. Remote Sens., 14.","DOI":"10.3390\/rs14020420"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.2991290","article-title":"A novel fast single image dehazing algorithm based on artificial multiexposure image fusion","volume":"70","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., and Lopez-Paz, D. (2017). mixup: Beyond empirical risk minimization. arXiv.","DOI":"10.1007\/978-1-4899-7687-1_79"},{"key":"ref_19","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ming, Y., and Zhang, R. (2018, January 12\u201316). Object detection and tracking based on recurrent neural networks. Proceedings of the 2018 14th IEEE International Conference on Signal Processing (ICSP), Beijing, China.","DOI":"10.1109\/ICSP.2018.8652389"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_22","unstructured":"Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y. (2013). Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part I 14.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_24","unstructured":"Solawetz, J. (2023, December 18). What is YOLOv8? The Ultimate Guide. Available online: https:\/\/blog.roboflow.com\/whats-new-in-yolov8\/."},{"key":"ref_25","unstructured":"Jocher, G., Stoken, A., Borovec, J., Chaurasia, A., Changyu, L., Hogan, A., Hajek, J., Diaconu, L., Kwon, Y., and Defretin, Y. (2021). Ultralytics\/yolov5: v5. 0-YOLOv5-P6 1280 Models, AWS, Supervise. ly and YouTube Integrations, Zenodo."},{"key":"ref_26","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Liao, H.Y.M., Wu, Y.H., Chen, P.Y., Hsieh, J.W., and Yeh, I.H. (2020, January 14\u201319). CSPNet: A new backbone that can enhance learning capability of CNN. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Virtual.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201322). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_30","unstructured":"Mnih, V., Heess, N., Graves, A., and Kavukcuoglu, K. (2014). Recurrent models of visual attention. arXiv."},{"key":"ref_31","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., and Kavukcuoglu, K. (2015). Spatial transformer networks. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Luo, Y., Qi, G., Meng, J., Li, Y., and Mazur, N. (2021). Remote sensing image defogging networks based on dual self-attention boost residual octave convolution. Remote Sens., 13.","DOI":"10.3390\/rs13163104"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 14\u201319). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhu, P., Wen, L., Du, D., Bian, X., Ling, H., Hu, Q., Nie, Q., Cheng, H., Liu, C., and Liu, X. (2018, January 8\u201314). Visdrone-det2018: The vision meets drone object detection in image challenge results. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_29"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018, January 18\u201322). DOTA: A large-scale dataset for object detection in aerial images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland. Proceedings, Part V 13.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_38","first-page":"1","article-title":"Align deep features for oriented object detection","volume":"60","author":"Han","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"25345","DOI":"10.1109\/TITS.2022.3158253","article-title":"Edge YOLO: Real-time intelligent object detection system based on edge-cloud cooperation in autonomous vehicles","volume":"23","author":"Liang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_40","unstructured":"Wang, X., Wang, G., Dang, Q., Liu, Y., Hu, X., and Yu, D. (2022). PP-YOLOE-R: An Efficient Anchor-Free Rotated Object Detector. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tang, W., Sun, J., and Wang, G. (2021, January 22\u201324). Horizontal Feature Pyramid Network for Object Detection in UAV Images. Proceedings of the 2021 China Automation Congress (CAC), Beijing, China.","DOI":"10.1109\/CAC53003.2021.9727887"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Akyon, F.C., Altinuc, S.O., and Temizel, A. (2022, January 16\u201319). Slicing aided hyper inference and fine-tuning for small object detection. Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France.","DOI":"10.1109\/ICIP46576.2022.9897990"},{"key":"ref_43","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as points. arXiv."},{"key":"ref_44","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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_45","unstructured":"Guo, X. (2023). A novel Multi to Single Module for small object detection. arXiv."},{"key":"ref_46","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Pan, X., Ren, Y., Sheng, K., Dong, W., Yuan, H., Guo, X., Ma, C., and Xu, C. (2020, January 13\u201319). Dynamic refinement network for oriented and densely packed object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01122"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.isprsjprs.2020.09.022","article-title":"Oriented objects as pairs of middle lines","volume":"169","author":"Wei","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ming, Q., Zhou, Z., Miao, L., Zhang, H., and Li, L. (2021, January 2\u20139). Dynamic anchor learning for arbitrary-oriented object detection. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual.","DOI":"10.1609\/aaai.v35i3.16336"},{"key":"ref_50","unstructured":"Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., and Fu, K. (November, January 27). Scrdet: Towards more robust detection for small, cluttered and rotated objects. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"8062","DOI":"10.1109\/JSEN.2020.2981719","article-title":"Image dehazing by an artificial image fusion method based on adaptive structure decomposition","volume":"20","author":"Zheng","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Luo, Y., Wei, H., Li, Y., Qi, G., Mazur, N., Li, Y., and Li, P. (2021). Atmospheric light estimation based remote sensing image dehazing. Remote Sens., 13.","DOI":"10.3390\/rs13132432"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/4\/644\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:57:40Z","timestamp":1760104660000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/4\/644"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,9]]},"references-count":52,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["rs16040644"],"URL":"https:\/\/doi.org\/10.3390\/rs16040644","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,9]]}}}