Abstract
Bird’s nest is a common defect in transmission line, which seriously affects the safe and stable operation of the line. This paper presents a method of bird’s nest hazard level identification based on improved yolov5 and location constraints, which solves the problem of bird’s nest multiple identification and hazard level classification. We integrate GhostModule and ECA to design a lightweight attention mechanism convolution module (LAMCM). The original yolov5 is improved by using LAMCM and adding a prediction head, which improves the detection ability of small targets and alleviates the negative impact of scale violence. We only identify the bird’s nest on the panorama of UAV patrol, and classify the hazard level of the bird’s nest according to the location constraints of the bird’s nest and insulator. Experiments on coco dataset and self built transmission line dataset (TL) show that our algorithm is superior to other commonly used algorithms. In particular, the recall rate of bird’s nest hazard level identification has increased significantly. Compared with the original yolov5, the recall rate of the three levels of bird’s nest improved by our proposed improved yolov5 is more than 3%.
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References
Howard, A., et al.: Searching for mobilenetv3. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1314–1324 (2019)
Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: Ghostnet: more features from cheap operations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1580–1589 (2020)
Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Supplementary material for ‘eca-net: efficient channel attention for deep convolutional neural networks. Technical report, Technical report
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Dai, J., Li,Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154–6162 (2018)
Liu, W., et al.: 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
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: 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)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: Yolox: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 (2021)
Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: Scaled-yolov4: scaling cross stage partial network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13029–13038 (2021)
Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Law, H., Deng, J.: CornerNet: detecting objects as paired keypoints. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 765–781. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_45
Zhou, X., Zhuo, J., Krahenbuhl, P.: Bottom-up object detection by grouping extreme and center points. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 850–859 (2019)
Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)
Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: beyound anchor-based object detection. IEEE Trans. Image Process. 29, 7389–7398 (2020)
Yang, Z., Liu, S., Hu, H., Wang, L., Lin, S.: Reppoints: point set representation for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9657–9666 (2019)
Wang, Q.: A solution for identification of bird’s nests on transmission lines with UAV patrol. In: 2016 International Conference on Artificial Intelligence and Engineering Applications, pp. 202–206. Atlantis Press (2016)
Ni, H., Wang, M., Zhao, L.: An improved faster R-CNN for defect recognition of key components of transmission line. Math. Biosci. Eng. 18(4), 4679–4695 (2021)
Li, J., Yan, D., Luan, K., Li, Z., Liang, H.: Deep learning-based bird’s nest detection on transmission lines using UAV imagery. Appl. Sci. 10(18), 6147 (2020)
Jianfeng, L., et al.: Detection of bird’s nest in high power lines in the vicinity of remote campus based on combination features and cascade classifier. IEEE Access 6, 39063–39071 (2018)
Hui, Z., Jian, Z., Yuran, C., Su, J., Di, W., Hao, D.: Intelligent bird’s nest hazard detection of transmission line based on retinanet model. In: Journal of Physics: Conference Series, volume 2005, page 012235. IOP Publishing (2021)
Yang, Q., Zhang, Z., Yan, L., Wang, W., Zhang, Y., Zhang, C.: Lightweight bird’s nest location recognition method based on yolov4-tiny. In: 2021 IEEE International Conference on Electrical Engineering and Mechatronics Technology (ICEEMT), pp. 402–405. IEEE (2021)
Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Liu, S., Huang, D., Wang, Y.: Learning spatial fusion for single-shot object detection. arXiv preprint arXiv:1911.09516 (2019)
Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019)
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Wu, Y., Zeng, Q., Li, P., Huang, W., Liang, L., Chen, J. (2022). Identification of Bird’s Nest Hazard Level of Transmission Line Based on Improved Yolov5 and Location Constraints. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_34
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