{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T16:54:06Z","timestamp":1769100846514,"version":"3.49.0"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100015401","name":"Key Research and Development Projects of Shaanxi Province","doi-asserted-by":"publisher","award":["2024GX-YBXM-555"],"award-info":[{"award-number":["2024GX-YBXM-555"]}],"id":[{"id":"10.13039\/501100015401","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s40747-025-01956-z","type":"journal-article","created":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T23:54:37Z","timestamp":1749426877000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["RL-Net: a rapid and lightweight network for detecting tiny vehicle targets in remote sensing images"],"prefix":"10.1007","volume":"11","author":[{"given":"Yaoyao","family":"Du","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9550-9779","authenticated-orcid":false,"given":"Li","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xingxing","family":"Hao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,9]]},"reference":[{"key":"1956_CR1","doi-asserted-by":"publisher","unstructured":"Muhmad\u00a0Kamarulzaman, A.M., Wan Mohd\u00a0Jaafar, W.S., Mohd\u00a0Said, M.N., Saad, S.N.M., Mohan, M.: Uav implementations in urban planning and related sectors of rapidly developing nations: A review and future perspectives for malaysia. Remote Sensing 15(11) (2023) https:\/\/doi.org\/10.3390\/rs15112845","DOI":"10.3390\/rs15112845"},{"key":"1956_CR2","doi-asserted-by":"publisher","unstructured":"Ning, Z., Hu, H., Wang, X., Guo, L., Guo, S., Wang, G., Gao, X.: Mobile edge computing and machine learning in the internet of unmanned aerial vehicles: A survey. ACM Comput. Surv. 56(1) (2023) https:\/\/doi.org\/10.1145\/3604933","DOI":"10.1145\/3604933"},{"key":"1956_CR3","doi-asserted-by":"publisher","unstructured":"Munawar, H.S., Hammad, A.W.A., Waller, S.T.: Remote sensing methods for flood prediction: A review. Sensors 22(3) (2022) https:\/\/doi.org\/10.3390\/s22030960","DOI":"10.3390\/s22030960"},{"issue":"4","key":"1956_CR4","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1016\/j.ejrs.2024.08.001","volume":"27","author":"SS Ganesh Kumar","year":"2024","unstructured":"Ganesh Kumar SS, Gudipalli A (2024) A comprehensive review on payloads of unmanned aerial vehicle. The Egyptian Journal of Remote Sensing and Space Sciences 27(4):637\u2013644. https:\/\/doi.org\/10.1016\/j.ejrs.2024.08.001","journal-title":"The Egyptian Journal of Remote Sensing and Space Sciences"},{"key":"1956_CR5","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2023.02.006","volume":"531","author":"V Kamath","year":"2023","unstructured":"Kamath V, Renuka A (2023) Deep learning based object detection for resource constrained devices: Systematic review, future trends and challenges ahead. Neurocomputing 531:34\u201360. https:\/\/doi.org\/10.1016\/j.neucom.2023.02.006","journal-title":"Neurocomputing"},{"key":"1956_CR6","doi-asserted-by":"publisher","first-page":"124848","DOI":"10.1016\/j.eswa.2024.124848","volume":"256","author":"C Xue","year":"2024","unstructured":"Xue C, Xia Y, Wu M, Chen Z, Cheng F, Yun L (2024) El-yolo: An efficient and lightweight low-altitude aerial objects detector for onboard applications. Expert Syst Appl 256:124848. https:\/\/doi.org\/10.1016\/j.eswa.2024.124848","journal-title":"Expert Syst Appl"},{"key":"1956_CR7","doi-asserted-by":"publisher","unstructured":"Iftikhar, S., Asim, M., Zhang, Z., Muthanna, A., Chen, J., El-Affendi, M., Sedik, A., Abd\u00a0El-Latif, A.A.: Target detection and recognition for traffic congestion in smart cities using deep learning-enabled uavs: A review and analysis. Applied Sciences 13(6) (2023) https:\/\/doi.org\/10.3390\/app13063995","DOI":"10.3390\/app13063995"},{"key":"1956_CR8","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.isprsjprs.2016.03.014","volume":"117","author":"G Cheng","year":"2016","unstructured":"Cheng G, Han J (2016) A survey on object detection in optical remote sensing images. ISPRS J Photogramm Remote Sens 117:11\u201328. https:\/\/doi.org\/10.1016\/j.isprsjprs.2016.03.014","journal-title":"ISPRS J Photogramm Remote Sens"},{"issue":"11","key":"1956_CR9","doi-asserted-by":"publisher","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","volume":"30","author":"Z-Q Zhao","year":"2019","unstructured":"Zhao Z-Q, Zheng P, Xu S-T, Wu X (2019) Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems 30(11):3212\u20133232. https:\/\/doi.org\/10.1109\/TNNLS.2018.2876865","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"1956_CR10","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.isprsjprs.2018.04.003","volume":"145","author":"Z Deng","year":"2018","unstructured":"Deng Z, Sun H, Zhou S, Zhao J, Lei L, Zou H (2018) Multi-scale object detection in remote sensing imagery with convolutional neural networks. ISPRS J Photogramm Remote Sens 145:3\u201322. https:\/\/doi.org\/10.1016\/j.isprsjprs.2018.04.003. (. Deep Learning RS Data)","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"1956_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2021.3098774","volume":"19","author":"X Li","year":"2022","unstructured":"Li X, He M, Li H, Shen H (2022) A combined loss-based multiscale fully convolutional network for high-resolution remote sensing image change detection. IEEE Geosci Remote Sens Lett 19:1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2021.3098774","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1956_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3095186","volume":"60","author":"Q Ming","year":"2022","unstructured":"Ming Q, Miao L, Zhou Z, Dong Y (2022) Cfc-net: A critical feature capturing network for arbitrary-oriented object detection in remote-sensing images. IEEE Trans Geosci Remote Sens 60:1\u201314. https:\/\/doi.org\/10.1109\/TGRS.2021.3095186","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"9","key":"1956_CR13","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904\u20131916. https:\/\/doi.org\/10.1109\/TPAMI.2015.2389824","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1956_CR14","doi-asserted-by":"publisher","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936\u2013 944 ( 2017). https:\/\/doi.org\/10.1109\/CVPR.2017.106","DOI":"10.1109\/CVPR.2017.106"},{"key":"1956_CR15","doi-asserted-by":"publisher","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and<0.5MB model size (2016). https:\/\/doi.org\/10.48550\/arXiv.1602.07360","DOI":"10.48550\/arXiv.1602.07360"},{"key":"1956_CR16","doi-asserted-by":"publisher","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017). https:\/\/doi.org\/10.48550\/arXiv.1704.04861","DOI":"10.48550\/arXiv.1704.04861"},{"key":"1956_CR17","doi-asserted-by":"publisher","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6848\u2013 6856 ( 2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00716","DOI":"10.1109\/CVPR.2018.00716"},{"key":"1956_CR18","doi-asserted-by":"publisher","first-page":"124282","DOI":"10.1016\/j.eswa.2024.124282","volume":"252","author":"B Yu","year":"2024","unstructured":"Yu B, Li Z, Cao Y, Wu C, Qi J, Wu L (2024) Yolo-mpam: Efficient real-time neural networks based on multi-channel feature fusion. Expert Syst Appl 252:124282. https:\/\/doi.org\/10.1016\/j.eswa.2024.124282","journal-title":"Expert Syst Appl"},{"key":"1956_CR19","doi-asserted-by":"publisher","unstructured":"Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, p. ( 2001). https:\/\/doi.org\/10.1109\/CVPR.2001.990517","DOI":"10.1109\/CVPR.2001.990517"},{"key":"1956_CR20","doi-asserted-by":"publisher","unstructured":"Dalal N, Triggs B Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905) 1:886\u20138931. https:\/\/doi.org\/10.1109\/CVPR.2005.177","DOI":"10.1109\/CVPR.2005.177"},{"issue":"3","key":"1956_CR21","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297. https:\/\/doi.org\/10.1023\/A:1022627411411","journal-title":"Mach Learn"},{"issue":"9","key":"1956_CR22","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2010","unstructured":"Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627\u20131645. https:\/\/doi.org\/10.1109\/TPAMI.2009.167","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1956_CR23","doi-asserted-by":"publisher","unstructured":"Ren, H., Li, Z.-N.: Object detection using edge histogram of oriented gradient. 2014 IEEE International Conference on Image Processing (ICIP), 4057\u20134061 (2014) https:\/\/doi.org\/10.1109\/ICIP.2014.7025824","DOI":"10.1109\/ICIP.2014.7025824"},{"key":"1956_CR24","doi-asserted-by":"publisher","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013 587 ( 2014). https:\/\/doi.org\/10.1109\/CVPR.2014.81","DOI":"10.1109\/CVPR.2014.81"},{"key":"1956_CR25","doi-asserted-by":"publisher","unstructured":"Girshick, R.: Fast r-cnn. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440\u2013 1448 ( 2015). https:\/\/doi.org\/10.1109\/ICCV.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"issue":"6","key":"1956_CR26","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149. https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1956_CR27","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.future.2021.04.019","volume":"123","author":"D Jiang","year":"2021","unstructured":"Jiang D, Li G, Tan C, Huang L, Sun Y, Kong J (2021) Semantic segmentation for multiscale target based on object recognition using the improved faster-rcnn model. Futur Gener Comput Syst 123:94\u2013104. https:\/\/doi.org\/10.1016\/j.future.2021.04.019","journal-title":"Futur Gener Comput Syst"},{"issue":"23","key":"1956_CR28","doi-asserted-by":"publisher","first-page":"18195","DOI":"10.1007\/s00500-023-09278-3","volume":"27","author":"Q Wu","year":"2023","unstructured":"Wu Q, Li X, Wang H (2023) Kang Bilal: Regional feature fusion for on-road detection of objects using camera and 3d-lidar in high-speed autonomous vehicles. Soft computing: A fusion of foundations, methodologies and applications 27(23):18195\u201318213. https:\/\/doi.org\/10.1007\/s00500-023-09278-3","journal-title":"Soft computing: A fusion of foundations, methodologies and applications"},{"key":"1956_CR29","doi-asserted-by":"publisher","unstructured":"Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: Unified, real-time object detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779\u2013788 (2015) https:\/\/doi.org\/10.1109\/CVPR.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"1956_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3051383","volume":"60","author":"X Li","year":"2022","unstructured":"Li X, Deng J, Fang Y (2022) Few-shot object detection on remote sensing images. IEEE Trans Geosci Remote Sens 60:1\u201314. https:\/\/doi.org\/10.1109\/TGRS.2021.3051383","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1956_CR31","doi-asserted-by":"publisher","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement (2018). https:\/\/doi.org\/10.48550\/arXiv.1804.02767","DOI":"10.48550\/arXiv.1804.02767"},{"key":"1956_CR32","doi-asserted-by":"publisher","first-page":"1742","DOI":"10.1109\/ACCESS.2023.3233964","volume":"11","author":"Z Liu","year":"2023","unstructured":"Liu Z, Gao Y, Du Q, Chen M, Lv W (2023) Yolo-extract: Improved yolov5 for aircraft object detection in remote sensing images. IEEE Access 11:1742\u20131751. https:\/\/doi.org\/10.1109\/ACCESS.2023.3233964","journal-title":"IEEE Access"},{"key":"1956_CR33","doi-asserted-by":"publisher","first-page":"30751","DOI":"10.1109\/JSEN.2023.3328990","volume":"23","author":"H Zhao","year":"2023","unstructured":"Zhao H, Chu K, Zhang J, Feng C (2023) Yolo-fsd: An improved target detection algorithm on remote-sensing images. IEEE Sens J 23:30751\u201330764. https:\/\/doi.org\/10.1109\/JSEN.2023.3328990","journal-title":"IEEE Sens J"},{"key":"1956_CR34","doi-asserted-by":"publisher","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 7464\u20137475 (2022) https:\/\/doi.org\/10.48550\/arXiv.2207.02696","DOI":"10.48550\/arXiv.2207.02696"},{"key":"1956_CR35","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.1109\/JSTARS.2023.3339235","volume":"17","author":"H Yi","year":"2024","unstructured":"Yi H, Liu B, Zhao B, Liu E (2024) Small object detection algorithm based on improved yolov8 for remote sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 17:1734\u20131747. https:\/\/doi.org\/10.1109\/JSTARS.2023.3339235","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"1956_CR36","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., Ding, G.: Yolov10: Real-time end-to-end object detection. ArXiv abs\/2405.14458 (2024)"},{"key":"1956_CR37","unstructured":"Khanam, R., Hussain, M.: Yolov11: An overview of the key architectural enhancements. ArXiv abs\/2410.17725 (2024)"},{"key":"1956_CR38","doi-asserted-by":"publisher","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8759\u2013 8768 ( 2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00913","DOI":"10.1109\/CVPR.2018.00913"},{"key":"1956_CR39","doi-asserted-by":"publisher","unstructured":"Yang, G., Lei, J., Zhu, Z., Cheng, S., Feng, Z., Liang, R.: Afpn: Asymptotic feature pyramid network for object detection. 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2184\u20132189 (2023) https:\/\/doi.org\/10.48550\/arXiv.2306.15988","DOI":"10.48550\/arXiv.2306.15988"},{"key":"1956_CR40","doi-asserted-by":"publisher","unstructured":"Chen, Y., Zhang, C., Chen, B., Huang, Y., Sun, Y., Wang, C., Fu, X., Dai, Y., Qin, F., Peng, Y., Gao, Y.: Accurate leukocyte detection based on deformable-detr and multi-level feature fusion for aiding diagnosis of blood diseases. Computers in Biology and Medicine 170, 107917 ( 2024) https:\/\/doi.org\/10.1016\/j.compbiomed.2024.107917","DOI":"10.1016\/j.compbiomed.2024.107917"},{"key":"1956_CR41","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. ArXiv abs\/2010.11929 (2020)"},{"key":"1956_CR42","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), 9992\u201310002 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1956_CR43","doi-asserted-by":"publisher","unstructured":"Xiao, Z., Tong, H.: Federated contrastive learning with feature-based distillation for human activity recognition. IEEE Transactions on Computational Social Systems, 1\u201314 (2025) https:\/\/doi.org\/10.1109\/TCSS.2024.3510428","DOI":"10.1109\/TCSS.2024.3510428"},{"issue":"1","key":"1956_CR44","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1109\/TETCI.2023.3304948","volume":"8","author":"Z Xiao","year":"2024","unstructured":"Xiao Z, Xing H, Zhao B, Qu R, Luo S, Dai P, Li K, Zhu Z (2024) Deep contrastive representation learning with self-distillation. IEEE Transactions on Emerging Topics in Computational Intelligence 8(1):3\u201315. https:\/\/doi.org\/10.1109\/TETCI.2023.3304948","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"1956_CR45","doi-asserted-by":"crossref","unstructured":"Cao, Z., Kooistra, L., Wang, W., Guo, L., Valente, J.: Real-time object detection based on uav remote sensing: A systematic literature review. Drones 7(10) (2023)","DOI":"10.3390\/drones7100620"},{"key":"1956_CR46","doi-asserted-by":"publisher","unstructured":"Liang, B., Luo, H.: Meanet: An effective and lightweight solution for salient object detection in optical remote sensing images. Expert Syst. Appl. 238(PA) (2024) https:\/\/doi.org\/10.1016\/j.eswa.2023.121778","DOI":"10.1016\/j.eswa.2023.121778"},{"key":"1956_CR47","doi-asserted-by":"publisher","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510\u2013 4520 ( 2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00474","DOI":"10.1109\/CVPR.2018.00474"},{"key":"1956_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3327285","volume":"61","author":"G Peng","year":"2023","unstructured":"Peng G, Yang Z, Wang S, Zhou Y (2023) Amflw-yolo: A lightweight network for remote sensing image detection based on attention mechanism and multiscale feature fusion. IEEE Trans Geosci Remote Sens 61:1\u201316. https:\/\/doi.org\/10.1109\/TGRS.2023.3327285","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1956_CR49","doi-asserted-by":"publisher","unstructured":"Tan, M., Pang, R., Le, Q.V.: Efficientdet: Scalable and efficient object detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10778\u2013 10787 ( 2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01079","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"1956_CR50","doi-asserted-by":"publisher","unstructured":"Wang, C.-Y., Yeh, I.-H., Liao, H.-Y.M.: YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information (2024). https:\/\/doi.org\/10.48550\/arXiv.2402.13616","DOI":"10.48550\/arXiv.2402.13616"},{"key":"1956_CR51","doi-asserted-by":"publisher","unstructured":"Qin, D., Leichner, C., Delakis, M., Fornoni, M., Luo, S., Yang, F., Wang, W., Banbury, C., Ye, C., Akin, B., Aggarwal, V., Zhu, T., Moro, D., Howard, A.: MobileNetV4 \u2013 Universal Models for the Mobile Ecosystem (2024). https:\/\/doi.org\/10.48550\/arXiv.2404.10518","DOI":"10.48550\/arXiv.2404.10518"},{"key":"1956_CR52","doi-asserted-by":"publisher","unstructured":"Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.-C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., Pang, R., Adam, H., Le, Q.: Searching for mobilenetv3. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1314\u2013 1324 ( 2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00140","DOI":"10.1109\/ICCV.2019.00140"},{"key":"1956_CR53","doi-asserted-by":"publisher","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: Ghostnet: More features from cheap operations. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1577\u2013 1586 ( 2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00165","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"1956_CR54","doi-asserted-by":"publisher","unstructured":"Wang, C.-Y., Liao, H.-Y.M., Yeh, I.-H., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W.: Cspnet: A new backbone that can enhance learning capability of cnn. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 1571\u20131580 (2019) https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00203","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"1956_CR55","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261\u2013 2269 ( 2017). https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"1956_CR56","doi-asserted-by":"publisher","unstructured":"Wang C-Y, Liao H, Yeh I-H (2022) Designing network design strategies through gradient path analysis. arXiv: abs\/2211.04800https:\/\/doi.org\/10.48550\/arXiv.2211.04800","DOI":"10.48550\/arXiv.2211.04800"},{"key":"1956_CR57","doi-asserted-by":"publisher","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., Sun, J.: Repvgg: Making vgg-style convnets great again. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13728\u2013 13737 ( 2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.01352","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"1956_CR58","doi-asserted-by":"publisher","unstructured":"Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800\u2013 1807 ( 2017). https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"1956_CR59","doi-asserted-by":"publisher","unstructured":"Zhu, J., He, G., Zhou, P.: Mfnet: A novel multilevel feature fusion network with multibranch structure for surface defect detection. IEEE Transactions on Instrumentation and Measurement 72, 1\u2013 11 ( 2023) https:\/\/doi.org\/10.1109\/TIM.2023.3284050","DOI":"10.1109\/TIM.2023.3284050"},{"key":"1956_CR60","doi-asserted-by":"publisher","unstructured":"Agarwal, S., Terrail, J.O.D., Jurie, F.: Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks (2019). https:\/\/doi.org\/10.48550\/arXiv.1809.03193","DOI":"10.48550\/arXiv.1809.03193"},{"key":"1956_CR61","doi-asserted-by":"publisher","unstructured":"Liu, W., Lu, H., Fu, H., Cao, Z.: Learning to upsample by learning to sample. 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), 6004\u20136014 (2023) https:\/\/doi.org\/10.48550\/arXiv.2308.15085","DOI":"10.48550\/arXiv.2308.15085"},{"key":"1956_CR62","doi-asserted-by":"publisher","unstructured":"Yang, W., Qiu, X.: A lightweight and efficient model for grape bunch detection and biophysical anomaly assessment in complex environments based on yolov8s. Frontiers in Plant Science 15 (2024) https:\/\/doi.org\/10.3389\/fpls.2024.1395796","DOI":"10.3389\/fpls.2024.1395796"},{"key":"1956_CR63","doi-asserted-by":"publisher","unstructured":"Wang, L., Lee, C.-Y., Tu, Z., Lazebnik, S.: Training Deeper Convolutional Networks with Deep Supervision (2015). https:\/\/doi.org\/10.48550\/arXiv.1505.02496","DOI":"10.48550\/arXiv.1505.02496"},{"key":"1956_CR64","doi-asserted-by":"publisher","unstructured":"Guo, C., Fan, B., Zhang, Q., Xiang, S., Pan, C.: Augfpn: Improving multi-scale feature learning for object detection. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12592\u201312601 (2019) https:\/\/doi.org\/10.1109\/CVPR42600.2020.01261","DOI":"10.1109\/CVPR42600.2020.01261"},{"issue":"11","key":"1956_CR65","doi-asserted-by":"publisher","first-page":"7380","DOI":"10.1109\/TPAMI.2021.3119563","volume":"44","author":"P Zhu","year":"2022","unstructured":"Zhu P, Wen L, Du D, Bian X, Fan H, Hu Q, Ling H (2022) Detection and tracking meet drones challenge. IEEE Trans Pattern Anal Mach Intell 44(11):7380\u20137399. https:\/\/doi.org\/10.1109\/TPAMI.2021.3119563","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1956_CR66","doi-asserted-by":"publisher","unstructured":"Wen, L., Du, D., Cai, Z., Lei, Z., Chang, M.-C., Qi, H., Lim, J., Yang, M.-H., Lyu, S.: Ua-detrac: A new benchmark and protocol for multi-object detection and tracking. Computer Vision and Image Understanding 193, 102907 ( 2020) https:\/\/doi.org\/10.1016\/j.cviu.2020.102907","DOI":"10.1016\/j.cviu.2020.102907"},{"key":"1956_CR67","doi-asserted-by":"publisher","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 618\u2013 626 ( 2017). https:\/\/doi.org\/10.1109\/ICCV.2017.74","DOI":"10.1109\/ICCV.2017.74"},{"key":"1956_CR68","unstructured":"Bochkovskiy, A., Wang, C., Liao, H.M.: Yolov4: Optimal speed and accuracy of object detection. CoRR abs\/2004.10934 (2020)"},{"key":"1956_CR69","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: YOLOX: exceeding YOLO series in 2021. CoRR abs\/2107.08430 (2021) arXiv: org\/abs\/2107.08430"},{"key":"1956_CR70","doi-asserted-by":"crossref","unstructured":"Varghese, R., M, S.: Yolov8: A novel object detection algorithm with enhanced performance and robustness. 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS), 1\u20136 (2024)","DOI":"10.1109\/ADICS58448.2024.10533619"},{"key":"1956_CR71","doi-asserted-by":"publisher","unstructured":"Chiu, Y.-C., Tsai, C.-Y., Ruan, M.-D., Shen, G.-Y., Lee, T.-T.: Mobilenet-ssdv2: An improved object detection model for embedded systems. In: 2020 International Conference On System Science And Engineering (Icsse), pp. 1\u2013 5 ( 2020). https:\/\/doi.org\/10.1109\/Icsse50014.2020.9219319","DOI":"10.1109\/Icsse50014.2020.9219319"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01956-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-01956-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01956-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T19:04:35Z","timestamp":1752779075000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-01956-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,9]]},"references-count":71,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["1956"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-01956-z","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,9]]},"assertion":[{"value":"14 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"328"}}