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| Defect Detection of Transmission Line Damper Based on Multi-Scale Convolutional Attention Mechanism |
| Zhang Ye1, Li Botao1, Shang Jinghao2, Huang Xinbo1, Zhai Pengchao1 |
1. School of Electronics and Information Xi’an Polytechnic University Xi’an 710048 China;
2. Xi'an Microelectronics Technology Institute Xi’an 710000 China |
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Abstract The presence of defective dampers in power transmission lines poses a significant risk to the secure and stable operation of the electrical grid. Advancing the intelligent development of damper inspections in transmission lines, a fast and accurate defect detection method holds paramount importance. Addressing the issue of insufficient damper defect recognition due to scarce defect samples, complex backgrounds, and varying regional dimensions, a novel damper defect detection network (RCA-YOLOv8) based on a multi-scale convolution attention mechanism was proposed.
Firstly, the diverse sizes of dampers in images are analyzed and a multi-scale convolution attention mechanism composed of three sets of bar-shaped convolutions is constructed to precisely capture features of different-sized dampers. Subsequently, a structural reparameterization method is utilized to convert the multi-branch structure in the network into a single-branch structure, enabling to maintain consistent inference speed with the single-branch structure while benefiting from the detection performance improvement brought by the multi-branch structure. In addition, based on the YOLOv8 feature extraction structure, a Conv Block structure containing Conv-A structure and structural reparameterization method was constructed to propose multi-scale features of dampers. Moreover, more shallow network features are integrated by using the AFPNs structure, resolving feature conflicts between large, medium, and small targets in the images, thereby enabling accurate detection of small damper targets and further enhancing detection performance. In this model, Conv-A is more able to focus on the dampers area in the image, reducing background interference, and structural reparameterization greatly reduces computational costs. AFPNs solve the problem of feature conflicts between large and small dampers in the image, thus achieving a low computational cost and high detection accuracy model.
For model experimentation, a dataset of damper defects in power transmission lines within substation scenes using image processing techniques is generated. To enhance dataset diversity and save annotation time, employ automatic data augmentation operations, including scale transformations, such as y-axis flipping, adding Gaussian noise, adding salt and pepper noise, adjusting brightness and non-scale transformations, such as size enlargement and reduction, along with automatically generated annotation files. Based on the dataset, the RCA-YOLOv8 network achieves an average precision mAP0.5 of 91.9% and mAP0.5:0.95 of 77.5%. Compared with other advanced one-stage and two-stage object detection networks, RCA-YOLOv8 has better damper defect detection performance. The mAP values of RCA-YOLOv8 network increased by 5.4% and 4.2% respectively compared to the basic network YOLOv8, with an inference speed of 60.88 frames per second under TITAN XP platform. It can be concluded that the proposed RCA-YOLOv8 network can rapidly and effectively detect dampers and their defects in power transmission lines.
The following conclusions can be drawn from the simulation analysis: (1) The network based on the multi-scale convolution attention mechanism can focus on crucial regions in the images, suppressing background regions' feature representations to obtain more relevant information. (2) Structural reparameterization successfully converts the multi-branch structure into a single-branch structure without any loss, striking a balance between detection accuracy and speed. (3) AFPNs with progressive feature fusion from different levels enable the network to achieve more precise detection of small damper targets.
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Received: 19 July 2023
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[1] 赵拥华, 张鹏, 段意, 等. FD型防振锤的动力特性研究[J]. 电力科学与工程, 2021, 37(5): 73-78.
Zhao Yonghua, Zhang Peng, Duan Yi, et al.Research on dynamic characteristics of FD anti-vibration hammer[J]. Electric Power Science and Engineering, 2021, 37(5): 73-78.
[2] 田毅, 伍逸群, 张烨, 等. 基于融合色差和神经网络的防震锤故障识别[J]. 计算机技术与发展, 2020, 30(8): 103-108.
Tian Yi, Wu Yiqun, Zhang Ye, et al.Fault identification of damper defect based on fused chromatic aberration and neural network[J]. Computer Technology and Development, 2020, 30(8): 103-108.
[3] Huang Xinbo, Zhang Xiaoling, Zhang Ye, et al.A method of identifying rust status of dampers based on image processing[J]. IEEE Transactions on Instru-mentation and Measurement, 2020, 69(8): 5407-5417.
[4] 隋宇, 宁平凡, 牛萍娟, 等. 面向架空输电线路的挂载无人机电力巡检技术研究综述[J]. 电网技术, 2021, 45(9): 3636-3648.
Sui Yu, Ning Pingfan, Niu Pingjuan, et al.Review on mounted UAV for transmission line inspection[J]. Power System Technology, 2021, 45(9): 3636-3648.
[5] 刘志颖, 缪希仁, 陈静, 等. 电力架空线路巡检可见光图像智能处理研究综述[J]. 电网技术, 2020, 44(3): 1057-1069.
Liu Zhiying, Miao Xiren, Chen Jing, et al.Review of visible image intelligent processing for transmission line inspection[J]. Power System Technology, 2020, 44(3): 1057-1069.
[6] Liu Wei, Anguelov D, Erhan D, et al.SSD: single shot multibox detector[C]//14th European Conference on Computer Vision - ECCV 2016, Amsterdam, the Netherlands, 2016: 21-37.
[7] Redmon J, Divvala S, Girshick R, et al.You only look once: unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016: 779-788.
[8] Redmon J, Farhadi A.YOLO9000: better, faster, stronger[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017: 7263-7271.
[9] Redmon J, Farhadi A. YOLOv3: an incremental improvement[J/OL]. arXiv, 2018: 1804.02767. http://doi.org/10.48550/arxiv.1804.02767.
[10] Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: optimal speed and accuracy of object detection[J/OL]. arXiv, 2020: 2004.10934. http://doi.org/10.48550/arXiv.2004.10934.
[11] 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[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, 2023: 7464-7475.
[12] Ren Shaoqing, He Kaiming, Girshick R, et al.Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[13] Cai Zhaowei, Vasconcelos N.Cascade R-CNN: delving into high quality object detection[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, 2018: 6154-6162.
[14] 赵洪山, 孙京杰, 彭轶灏, 等. 基于多尺度窗口和区域注意力残差网络的无线电力终端身份识别方法[J]. 电工技术学报, 2023, 38(1): 107-116.
Zhao Hongshan, Sun Jingjie, Peng Yihao, et al.Wireless power terminal identification method based on multiscale windowed deep residual network[J]. Transactions of China Electrotechnical Society, 2023, 38(1): 107-116.
[15] Hao Yanpeng, Liang Wei, Yang Lin, et al.Methods of image recognition of overhead power line insulators and ice types based on deep weakly-supervised and transfer learning[J]. IET Generation, Transmission & Distribution, 2022, 16(11): 2140-2153.
[16] 张焕龙, 齐企业, 张杰, 等. 基于改进YOLOv5的输电线路鸟巢检测方法研究[J]. 电力系统保护与控制, 2023, 51(2): 151-159.
Zhang Huanlong, Qi Qiye, Zhang Jie, et al.Bird nest detection method for transmission lines based on improved YOLOv5[J]. Power System Protection and Control, 2023, 51(2): 151-159.
[17] 宋立业, 刘帅, 王凯, 等. 基于改进EfficientDet的电网元件及缺陷识别方法[J]. 电工技术学报, 2022, 37(9): 2241-2251.
Song Liye, Liu Shuai, Wang Kai, et al.Identification method of power grid components and defects based on improved EfficientDet[J]. Transactions of China Electrotechnical Society, 2022, 37(9): 2241-2251.
[18] 苟军年, 杜愫愫, 刘力. 基于改进掩膜区域卷积神经网络的输电线路绝缘子自爆检测[J]. 电工技术学报, 2023, 38(1): 47-59.
Gou Junnian, Du Susu, Liu Li.Transmission line insulator self-explosion detection based on improved mask region-convolutional neural network[J]. Tran-sactions of China Electrotechnical Society, 2023, 38(1): 47-59.
[19] 李斌, 屈璐瑶, 朱新山, 等. 基于多尺度特征融合的绝缘子缺陷检测[J]. 电工技术学报, 2023, 38(1): 60-70.
Li Bin, Qu Luyao, Zhu Xinshan, et al.Insulator defect detection based on multi-scale feature fusion[J]. Transactions of China Electrotechnical Society, 2023, 38(1): 60-70.
[20] 杨帆, 王梦珺, 谭天, 等. 基于目标像素宽度识别的电力设备红外成像单目测距改进算法[J]. 电工技术学报, 2023, 38(8): 2244-2254.
Yang Fan, Wang Mengjun, Tan Tian, et al.An improved monocular ranging method for infrared image of power equipment based on the pixel width recognition of objects[J]. Transactions of China Electrotechnical Society, 2023, 38(8): 2244-2254.
[21] 左国玉, 马蕾, 徐长福, 等. 基于跨连接卷积神经网络的绝缘子检测方法[J]. 电力系统自动化, 2019, 43(4): 101-106.
Zuo Guoyu, Ma Lei, Xu Changfu, et al.Insulator detection method based on cross-connected convolutional neural network[J]. Automation of Electric Power Systems, 2019, 43(4): 101-106.
[22] 汤璐, 王淑青, 王年涛, 等. 基于改进YOLOX网络的雾天绝缘子缺陷检测[J]. 高压电器, 2024, 60(3): 223-228.
Tang Lu, Wang Shuqing, Wang Niantao, et al.Insulator defect detection in foggy condition based on improved YOLOX network[J]. High Voltage Apparatus, 2024, 60(3): 223-228.
[23] Bao Wenxia, Ren Yangxun, Wang Nian, et al.Detection of abnormal vibration dampers on transmission lines in UAV remote sensing images with PMA-YOLO[J]. Remote Sensing, 2021, 13(20): 4134.
[24] 赵振兵, 李延旭, 甄珍, 等. 结合KL散度和形状约束的Faster R-CNN典型金具检测方法[J]. 高电压技术, 2020, 46(9): 3018-3026.
Zhao Zhenbing, Li Yanxu, Zhen Zhen, et al.Typical fittings detection method with faster R-CNN combining KL divergence and shape constraints[J]. High Voltage Engineering, 2020, 46(9): 3018-3026.
[25] Liang Huagang, Zuo Chao, Wei Wangmin.Detection and evaluation method of transmission line defects based on deep learning[J]. IEEE Access, 2020, 8: 38448-38458.
[26] Ding Xiaohan, Zhang Xiangyu, Ma Ningning, et al.RepVGG: making VGG-style ConvNets great again[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021: 13733-13742.
[27] Yang Guoyu, Lei Jie, Zhu Zhikuan, et al. AFPN: asymptotic feature pyramid network for object detection[J/OL]. arXiv, 2023: 2306.15988. http://doi.org/10.48550/arXiv.2306.15988.
[28] Liu Songtao, Huang Di, Wang Yunhong. Learning spatial fusion for single-shot object detection[J/OL]. arXiv, 2019: 1911.09516. http://doi.org/10.48550/arXiv.1911.09516.
[29] 王建, 吴昊, 张博, 等. 不平衡样本下基于迁移学习-AlexNet的输电线路故障辨识方法[J]. 电力系统自动化, 2022, 46(22): 182-191.
Wang Jian, Wu Hao, Zhang Bo, et al.Fault identification method for transmission line based on transfer learning-AlexNet with imbalanced samples[J]. Automation of Electric Power Systems, 2022, 46(22): 182-191.
[30] Tan Mingxing, Pang Ruoming, Le Q V.EfficientDet: scalable and efficient object detection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020: 10781-10790.
[31] Zhang Xiaosong, Wan Fang, Liu Chang, et al.Learning to match anchors for visual object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022: 44(6): 3096-3109.
[32] Selvaraju R R, Cogswell M, Das A, et al.Grad-CAM: visual explanations from deep networks via gradient-based localization[C]//2017 IEEE International Conference on Computer Vision (ICCV), Venice, 2017: 618-626.
[33] Hu Jie, Shen Li, Sun Gang.Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, 2018: 7132-7141.
[34] Woo S, Park J, Lee J Y, et al.CBAM: convolutional block attention module[C]//15th European Conference on Computer Vision - ECCV 2018, Munich, Germany, 2018: 3-19.
[35] Hou Qibin, Zhou Daquan, Feng Jiashi.Coordinate attention for efficient mobile network design[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021: 13713-13722. |
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2024, Vol. 39 
