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FE-YOLOv5: Improved YOLOv5 Network for Multi-scale Drone-Captured Scene Detection

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Neural Information Processing (ICONIP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14448))

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Abstract

Due to the different angles and heights of UAV shooting, the shooting environment is complex, and the shooting targets are mostly small, so the target detection task in the drone-captured scene is still challenging. In this study, we present a highly precise technique for identifying objects in scenes captured by drones, which we refer to as FE-YOLOv5. First, to optimize cross-scale feature fusion and maximize the utilization of shallow feature information, we propose a novel feature pyramid model called MSF-BiFPN as our primary approach. Furthermore, to improve the fusion of features at different scales and boost their representational power, our innovative approach proposes an adaptive attention module. Moreover, we propose a novel feature enhancement module that effectively strengthens high-level features before feature fusion. This module effectively minimized feature loss during the fusion process, ultimately resulting in enhanced detection accuracy. Finally, the utilization of the normalized Wasserstein distance serves as a novel metric for enhancing the model’s sensitivity and accuracy in detecting small targets. The experimental results of FE-YOLOv5 on the VisDrone data set show that mAP 0.5 has increased by 7.8\(\%\), and mAP 0.5:0.95 increased by 5.7\(\%\). At the same time, the training results of the model at \(960 \times 960\) image resolution are better than the current YOLO series models, among which mAP 0.5 can reach 56.3\(\%\). Based on the experiments conducted, it has been demonstrated that the FE-YOLOv5 model effectively enhances the accuracy of object detection in UAV capture scenes.

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References

  1. Audebert, N., Le Saux, B., Lefèvre, S.: Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks. ISPRS J. Photogramm. Remote. Sens. 140, 20–32 (2018)

    Article  Google Scholar 

  2. Gu, J., Su, T., Wang, Q., et al.: Multiple moving targets surveillance based on a cooperative network for multi-UAV. IEEE Commun. Mag. 56(4), 82–89 (2018)

    Article  Google Scholar 

  3. Hird, J.N., Montaghi, A., McDermid, G.J., et al.: Use of unmanned aerial vehicles for monitoring the recovery of forest vegetation on petroleum well sites. Remote Sensing 9(5), 413 (2017)

    Article  Google Scholar 

  4. Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

    Google Scholar 

  5. Girshick R. Fast r-cnn[C]//Proceedings of the IEEE international conference on computer vision. 2015: 1440–1448

    Google Scholar 

  6. Ren S, He K, Girshick R, et al. Faster r-cnn: towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28 (2015)

    Google Scholar 

  7. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  8. Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

    Google Scholar 

  9. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  10. Everingham, M., Van Gool, L., Williams, C.K.I., et al.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vision 88, 303–338 (2010)

    Article  Google Scholar 

  11. Jocher, G., Stoken, A., Borovec, J., et al.: ultralytics/yolov5: v5. 0-YOLOv5-P6 1280 models, AWS, Supervise. ly and YouTube integrations. Zenodo (2021)

    Google Scholar 

  12. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

  13. Wang, C.Y., Liao, H.Y.M., Wu, Y.H., et al.: CSPNet: a new backbone that can enhance learning capability of CNN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 390–391 (2020)

    Google Scholar 

  14. Tan, M., Pang, R., Le, Q.V.: Efficientdet: scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781–10790 (2020)

    Google Scholar 

  15. Liu, S., Qi, L., Qin, H., et al.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768 (2018)

    Google Scholar 

  16. Wang, J., Xu, C., Yang, W., et al.: A normalized Gaussian Wasserstein distance for tiny object detection. arXiv preprint arXiv:2110.13389 (2021)

  17. Que, J.F., Peng, H.F., Xiong, J.Y.: Low altitude, slow speed and small size object detection improvement in noise conditions based on mixed training. J. Phys. Conf. Ser. IOP Publishing 1169(1), 012029 (2019)

    Google Scholar 

  18. Zhang, Z., Lu, X., Cao, G., et al.: ViT-YOLO: transformer-based YOLO for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2799–2808 (2021)

    Google Scholar 

  19. Solovyev, R., Wang, W., Gabruseva, T.: Weighted boxes fusion: ensembling boxes from different object detection models. Image Vis. Comput. 107, 104117 (2021)

    Article  Google Scholar 

  20. Woo, S., Park, J., Lee, J.Y., et al.: Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)

    Google Scholar 

  21. Lin, T.Y., Dollár, P., Girshick, R., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

    Google Scholar 

  22. Luo, Y., Cao, X., Zhang, J., et al.: CE-FPN: enhancing channel information for object detection. Multimed. Tools Appl. 81(21), 30685–30704 (2022)

    Article  Google Scholar 

  23. Shi, W., Caballero, J., Huszár, F., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874–1883 (2016)

    Google Scholar 

  24. Zhu, P., Wen, L., Bian, X., et al.: Vision meets drones: a challenge. arXiv preprint arXiv:1804.07437 (2018)

  25. He, K., Zhang, X., Ren, S., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)

    Article  Google Scholar 

  26. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

    Google Scholar 

  27. Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)

  28. Rezatofighi, H., Tsoi, N., Gwak, J.Y., et al.: Generalized intersection over union: a metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658–666 (2019)

    Google Scholar 

  29. Li, C., Li, L., Jiang, H., et al.: YOLOv6: a single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976 (2022)

  30. 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. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464–7475 (2023)

    Google Scholar 

  31. Zhu, X., Su, W., Lu, L., et al.: Deformable detr: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020)

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Correspondence to Zhiyan Dong .

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Zhao, C., Yan, Z., Dong, Z., Yang, D., Zhang, L. (2024). FE-YOLOv5: Improved YOLOv5 Network for Multi-scale Drone-Captured Scene Detection. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14448. Springer, Singapore. https://doi.org/10.1007/978-981-99-8082-6_23

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  • DOI: https://doi.org/10.1007/978-981-99-8082-6_23

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