UAV-YOLOv5: A Lightweight Object Detection Algorithm on Drone-captured Scenarios

Guoliang Li; Enguang Liu; Yanhong Wang; Yang Yang; Haiying Liu

doi:10.54691/2rsav890

Authors

Guoliang Li
Enguang Liu
Yanhong Wang
Yang Yang
Haiying Liu

DOI:

https://doi.org/10.54691/2rsav890

Keywords:

Object Detection; Small Object; Drone; YOLO.

Abstract

Aiming at the problems of common object detection algorithms on drone-captured scenarios, such as too large model, difficult deployment, low accuracy of small-scale object detection, this paper proposed a series of improved methods based on YOLOv5, which effectively improved the performance of the algorithm on drone-captured scenarios. A new dual-branch CSPNet (DB-CSPNet) structure was proposed, which effectively reduced the complexity and computation of the model. A new feature fusion path (FS-FPN) was proposed, which effectively improved the detection accuracy of the model. By integrating a attention mechanism (ACmix), the performance of the model is effectively improved. The experimental results shown that the proposed methods have a significant improvement effect on the accuracy of the object detection algorithm on drone-captured scenarios. The mAP@0.5 and mAP@0.5:0.95 of the algorithm which used the method 2 and 3 proposed in this paper can be improved by 2.5% and 1.6%. At the same time, the method 1 proposed in this paper can also achieve good lightweight effect, the model parameters and FLOPs can be reduced by 26.6% and 30.4%. The UAV-YOLOv5 implemented by all methods in this paper can also achieve a good balance between precision and lightweight. Compared with the default YOLOv5s, the mAP@0.5 and mAP@0.5:0.95 increased by 1.5% and 1.0%, and the parameters and FLOPs decreased by 3.7% and 7.0% respectively.

Downloads

Download data is not yet available.

References

Xia, G.S.; Bai, X.; Ding, J.; Zhu, Z.; Belongie, S.; Luo, J.; Datcu, M.; Pelillo, M.; Zhang, L. DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. In Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.

Deng, J.; Dong, W.; Socher, R.; Li, L.J.; Li, K.; Fei-Fei, L. ImageNet: A large-scale hierarchical image database. In Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248–255. https://doi.org/10.1109/CVPR.2009.5206848.

Lin, T.Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; Zitnick, C.L. Microsoft coco: Common objects in context. In Proceedings of the European conference on computer vision (ECCV), 2014, pp. 740–755.

Glenn, J. 6.2 - YOLOv5 Classification Models, Apple M1, Reproducibility, ClearML and Deci.ai integrations, 2022. https: //github.com/ultralytics/yolov5/releases/tag/v6.2.

Wang, C.Y.; Liao, H.Y.M.; Wu, Y.H.; Chen, P.Y.; Hsieh, J.W.; Yeh, I.H. CSPNet: A New Backbone That Can Enhance Learning Capability of CNN. In Proceedings of the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) Workshops, June 2020.

Pan, X.; Ge, C.; Lu, R.; Song, S.; Chen, G.; Huang, Z.; Huang, G. On the Integration of Self-Attention and Convolution. In Proceedings of the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022, pp. 815–825.

Ding, X.; Zhang, X.; Ma, N.; Han, J.; Ding, G.; Sun, J. RepVGG: Making VGG-Style ConvNets Great Again. In Proceedings of the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2021, pp. 13733–13742.

Bochkovskiy, A.; Wang, C.Y.; Liao, H.Y.M. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.109342020.

He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. In Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.

Xie, S.; Girshick, R.; Dollar, P.; Tu, Z.; He, K. Aggregated Residual Transformations for Deep Neural Networks. In Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.

Lin, T.Y.; Dollar, P.; Girshick, R.; He, K.; Hariharan, B.; Belongie, S. Feature Pyramid Networks for Object Detection. In Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.

Liu, S.; Qi, L.; Qin, H.; Shi, J.; Jia, J. Path Aggregation Network for Instance Segmentation. In Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.

Tan, M.; Pang, R.; Le, Q.V. EfficientDet: Scalable and Efficient Object Detection. In Proceedings of the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.

Ghiasi, G.; Lin, T.Y.; Le, Q.V. NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection. In Proceedings of the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.

Zhu, P.; Wen, L.; Du, D.; Bian, X.; Fan, H.; Hu, Q.; Ling, H. Detection and Tracking Meet Drones Challenge. IEEE Transactions on Pattern Analysis and Machine Intelligence 2021, pp. 1–1. https://doi.org/10.1109/TPAMI.2021.3119563.