In response to the challenges of small object detection in UAV aerial photography, such as complex backgrounds, tiny targets, dense targets, and edge deployment, the YOLOv11n model was improved. Specifically, an EfficiBackbone module was designed for the backbone part, the C3K2 was improved using the RipViT block in the Neck part, and the original detection head was replaced with a dynamic detection head. The improved YOLOv11 network was thus completed. Experimental results show that the model has significantly improved mAP@0.5 and mAP@0.5:0.95 on the VisDrone2019 dataset, proving the effectiveness of the model.
References
[1]
Mohsan, S.A.H., Khan, M.A., Noor, F., Ullah, I. and Alsharif, M.H. (2022) Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review. Drones, 6, Article No. 147. https://doi.org/10.3390/drones6060147
[2]
Zhou, X., Koltun, V. and Krähenbühl, P. (2021) Probabilistic Two-Stage Detection.
[3]
Yang, Z., Gong, B., Wang, L., Huang, W., Yu, D. and Luo, J. (2019). A Fast and Accurate One-Stage Approach to Visual Grounding. 2019 IEEE/CVFInternationalConferenceonComputerVision (ICCV), Seoul, 27 October-2 November 2019, 4682-4692. https://doi.org/10.1109/iccv.2019.00478
[4]
Gang D, Weicheng X, Xiaolong H, etal. (2023) Review of Small Object Detection Algorithms Based on Deep Learning. JournalofComputerEngineering&Applications, 59.
[5]
Zeng, Y., Zhang, T., He, W. and Zhang, Z. (2023) Yolov7-UAV: An Unmanned Aerial Vehicle Image Object Detection Algorithm Based on Improved Yolov7. Electronics, 12, Article No. 3141. https://doi.org/10.3390/electronics12143141
[6]
Li, Y., Fan, Q., Huang, H., Han, Z. and Gu, Q. (2023) A Modified Yolov8 Detection Network for UAV Aerial Image Recognition. Drones, 7, Article No. 304. https://doi.org/10.3390/drones7050304
[7]
Li, Y., Li, Q., Pan, J., Zhou, Y., Zhu, H., Wei, H., et al. (2024) SOD-YOLO: Small-Object-Detection Algorithm Based on Improved YOLOv8 for UAV Images. RemoteSensing, 16, Article No. 3057. https://doi.org/10.3390/rs16163057
[8]
Khanam, R. and Hussain, M. (2024) Yolov11: An Overview of the Key Architectural Enhancements.
[9]
Mahasin, M. and Dewi, I.A. (2022) Comparison of CSPDarkNet53, CSPResNeXt-50, and EfficientNet-B0 Backbones on YOLO v4 as Object Detector. InternationalJournalofEngineering, ScienceandInformationTechnology, 2, 64-72. https://doi.org/10.52088/ijesty.v2i3.291
[10]
Wang, G., Chen, Y., An, P., Hong, H., Hu, J. and Huang, T. (2023) UAV-Yolov8: A Small-Object-Detection Model Based on Improved Yolov8 for UAV Aerial Photography Scenarios. Sensors, 23, Article No. 7190. https://doi.org/10.3390/s23167190
[11]
Liu, X., Peng, H., Zheng, N., Yang, Y., Hu, H. and Yuan, Y. (2023) EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, 17-24 June 2023, 14420-14430. https://doi.org/10.1109/cvpr52729.2023.01386
[12]
Wang, A., Chen, H., Lin, Z., Han, J. and Ding, G. (2024) Rep ViT: Revisiting Mobile CNN From ViT Perspective. 2024 IEEE/CVFConferenceonComputerVisionandPatternRecognition (CVPR), Seattle, 16-22 June 2024, 15909-15920. https://doi.org/10.1109/cvpr52733.2024.01506
[13]
Dai, X., Chen, Y., Xiao, B., Chen, D., Liu, M., Yuan, L., et al. (2021) Dynamic Head: Unifying Object Detection Heads with Attentions. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, 20-25 June 2021, 7369-7378. https://doi.org/10.1109/cvpr46437.2021.00729
[14]
Du, D., Zhu, P., Wen, L., et al. (2019) VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results. ProceedingsoftheIEEE/CVF International Conference on Computer Vision Workshops.
[15]
Yue, Y., Finley, T., Radlinski, F. and Joachims, T. (2007) A Support Vector Method for Optimizing Average Precision. Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Amsterdam, 23-27 July 2007, 271-278. https://doi.org/10.1145/1277741.1277790
[16]
Lee, Y., Waterman, A., Avizienis, R., Cook, H., Sun, C., Stojanovic, V., et al. (2014) A 45 nm 1.3 GHz 16.7 Double-Precision GFLOPS/W RISC-V Processor with Vector Accelerators. ESSCIRC 2014 40th European Solid State Circuits Conference (ESSCIRC), Venice Lido, 22-26 September 2014, 199-202. https://doi.org/10.1109/esscirc.2014.6942056
[17]
Jocher, G., Stoken, A., Chaurasia, A., et al. (2021) Ultralytics/Yolov5: v6.0-YOLOv5n “Nano” Models, Roboflow Integration, TensorFlow Export, OpenCV DNN Support. Zenodo.
[18]
Chu, X., Zheng, A., Zhang, X. and Sun, J. (2020) Detection in Crowded Scenes: One Proposal, Multiple Predictions. 2020 IEEE/CVFConferenceonComputerVisionandPatternRecognition (CVPR), Seattle, 13-19 June 2020, 12211-12220. https://doi.org/10.1109/cvpr42600.2020.01223
[19]
Li, Z., Wang, Z. and He, Y. (2023) Aerial Photography Dense Small Target Detection Algorithm Based on Adaptive Collaborative Attention Mechanism. J Aeronaut, 10, 1-12.
