Decoupled Sematic Distance Based Multi-class Defect Scene Detecting for Substations

doi:10.11930/j.issn.1004-9649.202208117

Abstract

Abstract: Due to the complexity and differences of defect types in substations, traditional deep learning models for defects detection lack comprehensive response ability. It proposes a sematic distance based decoupling detection model. Firstly, the decoupled model structure is determined by clustering defect classes according to the semantic information distance between each other. Then, the weighted anchor fusion and local prediction loss techniques are used to improve the model performance. Meanwhile, the decoupled non-maximum suppression strategy is proposed to accelerate the model inference process. The experiment results show that the mean average precision of the model reaches 69.68%. Compared with YOLOX, which has been recognized as the best real-time object detection model, the accuracy of proposed model is improved by 1.36 percentage points, the parameter quantity is reduced by 5%, and the inference speed is improved by 34%.

Key words: substation maintain and operation scene, defect detection, deep learning, semantic information distance, decoupled model

ZHANG Xin, YE Junjie, CUI Yao, HUANG Xin, ZHONG Linlin. Decoupled Sematic Distance Based Multi-class Defect Scene Detecting for Substations[J]. Electric Power, 2023, 56(6): 209-218.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202208117

https://www.electricpower.com.cn/EN/Y2023/V56/I6/209

References

[1] 罗琨, 时永肖, 李正新, 等. 智能变电站继电保护装置寿命模型及其辨识方法[J]. 智慧电力, 2021, 49(1): 96–101
LUO Kun, SHI Yongxiao, LI Zhengxin, et al. Life model and identification method of relay protection device in smart substation[J]. Smart Power, 2021, 49(1): 96–101
[2] 李肖博, 于杨, 姚浩, 等. 新一代智能变电站采控装置[J]. 中国电力, 2022, 55(4): 85–92
LI Xiaobo, YU Yang, YAO Hao, et al. Sample-control-device of smart substation[J]. Electric Power, 2022, 55(4): 85–92
[3] 李泽文, 王志刚, 穆利智, 等. 变电站智能安监可穿戴设备设计[J]. 电力科学与技术学报, 2022, 37(4): 217–226
LI Zewen, WANG Zhigang, MU Lizhi, et al. Design of wearable equipment for substation intelligent safety supervision[J]. Journal of Electric Power Science and Technology, 2022, 37(4): 217–226
[4] 李自若, 沈曦, 张亦兵, 等. 基于深度强化学习的智慧变电站网络异常检测方法[J]. 南方电网技术, 2021, 15(6): 98–105
LI Ziruo, SHEN Xi, ZHANG Yibing, et al. Network anomaly detection method for smart substation based on deep reinforcement learning[J]. Southern Power System Technology, 2021, 15(6): 98–105
[5] 高熠, 田联房, 杜启亮. 基于Mask R-CNN的复合绝缘子过热缺陷检测[J]. 中国电力, 2021, 54(1): 135–141
GAO Yi, TIAN Lianfang, DU Qiliang. Overheating defect detection of composite insulator based on mask R-CNN[J]. Electric Power, 2021, 54(1): 135–141
[6] 王帅, 姜敏, 李江林, 等. 全维度智能变电站设备状态监测关键技术研究[J]. 电测与仪表, 2020, 57(7): 82–86
WANG Shuai, JIANG Min, LI Jianglin, et al. Research on key technologies of condition monitoring of full-dimensional intelligent substation equipment[J]. Electrical Measurement & Instrumentation, 2020, 57(7): 82–86
[7] 刘颖, 胡楠, 杨壮观, 等. 基于深度学习的电网监控视频中工作人员检测与识别[J]. 沈阳工业大学学报, 2019, 41(5): 544–548
LIU Ying, HU Nan, YANG Zhuangguan, et al. Detection and identification of staff in power grid monitoring video based on deep learning[J]. Journal of Shenyang University of Technology, 2019, 41(5): 544–548
[8] 王旭红, 李浩, 樊绍胜, 等. 基于改进SSD的电力设备红外图像异常自动检测方法[J]. 电工技术学报, 2020, 35(增刊1): 302–310
WANG Xuhong, LI Hao, FAN Shaosheng, et al. Infrared image anomaly automatic detection method for power equipment based on improved single shot multi box detection[J]. Transactions of China Electrotechnical Society, 2020, 35(S1): 302–310
[9] 王凯, 王健, 刘刚, 等. 基于RetinaNet和类别平衡采样方法的销钉缺陷检测[J]. 电力工程技术, 2019, 38(4): 80–85
WANG Kai, WANG Jian, LIU Gang, et al. Defect detection of pins based on RetinaNet and class balanced sampling methods[J]. Electric Power Engineering Technology, 2019, 38(4): 80–85
[10] 黄锐勇, 戴美胜, 郑跃斌, 等. 电力设备红外图像缺陷检测[J]. 中国电力, 2021, 54(2): 147–155
HUANG Ruiyong, DAI Meisheng, ZHENG Yuebin, et al. Defect detection of power equipment by infrared image[J]. Electric Power, 2021, 54(2): 147–155
[11] 位一鸣, 童力, 罗麟, 等. 基于卷积神经网络的主变压器外观缺陷检测方法[J]. 浙江电力, 2019, 38(4): 61–68
WEI Yiming, TONG Li, LUO Lin, et al. An exterior defects detecting method of main transformer based on convolutional neural networks[J]. Zhejiang Electric Power, 2019, 38(4): 61–68
[12] 张晶焯, 佘楚云, 伍国兴, 等. 基于增强特征金字塔和可变形卷积的绝缘子缺陷检测[J]. 电力工程技术, 2021, 40(4): 155–160
ZHANG Jingzhuo, SHE Chuyun, WU Guoxing, et al. Insulator defect detection based on enhanced feature pyramid and deformable convolution[J]. Electric Power Engineering Technology, 2021, 40(4): 155–160
[13] 罗鹏, 王波, 马恒瑞, 等. 基于组合式目标检测框架的低漏报率缺陷识别方法[J]. 高电压技术, 2021, 47(2): 454–464
LUO Peng, WANG Bo, MA Hengrui, et al. Defect recognition method with low false negative rate based on combined target detection framework[J]. High Voltage Engineering, 2021, 47(2): 454–464
[14] 顾晓东, 唐丹宏, 黄晓华. 基于深度学习的电网巡检图像缺陷检测与识别[J]. 电力系统保护与控制, 2021, 49(5): 91–97
GU Xiaodong, TANG Danhong, HUANG Xiaohua. Deep learning-based defect detection and recognition of a power grid inspection image[J]. Power System Protection and Control, 2021, 49(5): 91–97
[15] 应樱. 基于改进Faster R-CNN的变电站设备缺陷检测算法研究[D]. 杭州: 浙江大学, 2021.
YING Ying. Research on defect detection algorithm of substation equipment based on improved Faster R-CNN[D]. Hangzhou: Zhejiang University, 2021.
[16] 尹思宇. 深度学习在变电站电力设备缺陷检测中的应用研究[D]. 成都: 电子科技大学, 2020.
YIN Siyu. Research on application of deep learning in defect detection of substation power equipment[D]. Chengdu: University of Electronic Science and Technology of China, 2020.
[17] 徐海洋. 变电站设备表面缺陷图像识别关键技术研究[D]. 南宁: 广西大学, 2021.
XU Haiyang. Research on key technologies of image recognition for surface defects of substation equipment[D]. Nanning: Guangxi University, 2021.
[18] GE Z, LIU S, WANG F, et al. YOLOX: exceeding YOLO series in 2021[EB/OL]. 2021: arXiv: 2107.08430.https://arxiv.org/abs/2107.08430.
[19] WANG C Y, LIAO H Y M, WU Y H, et al. CSPNet: a new backbone that can enhance learning capability of CNN[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Seattle, WA, USA. IEEE, 2020: 1571–1580.
[20] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. 2020: arXiv: 2004.10934.https://arxiv.org/abs/2004.10934.
[21] REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL]. 2018: arXiv: 1804.02767.https://arxiv.org/abs/1804.02767.
[22] SALEH S, PAUL M, GREG F. “How many images do I need?” Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring[J]. Ecological Informatics, 2020, 57: 101085.
[23] HE K M, ZHANG X Y, REN S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904–1916.
[24] BODLA N, SINGH B, CHELLAPPA R, et al. Soft-NMS—improving object detection with one line of code[C]//2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy. IEEE, 2017: 5562–5570.
[25] DUAN K W, BAI S, XIE L X, et al. CenterNet: keypoint triplets for object detection[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South). IEEE, 2020: 6568–6577.