[1]WANG Wei,YANG Yaoquan,WANG Qianming,et al.A multi-category fitting detection method with embedded visual relation masks[J].CAAI Transactions on Intelligent Systems,2023,18(3):440-449.[doi:10.11992/tis.202202021]
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A multi-category fitting detection method with embedded visual relation masks

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 3 Page number: 440-449 Column: 学术论文—机器学习 Public date: 2023-07-05
Title:
A multi-category fitting detection method with embedded visual relation masks
Author(s):
WANG Wei1;  YANG Yaoquan1;  WANG Qianming1;  ZHAI Yongjie1;  ZHAO Zhenbing2
1. Department of Automation, North China Electric Power University, Baoding 071003, China;
2. Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China
Keywords:
target detection; transmission line; fittings; deep learning; visual relationship; prior knowledge; spatial information; auxiliary information
CLC:
TP183
DOI:
10.11992/tis.202202021
Abstract:
In the process of detecting power transmission line fittings, due to the influence of complex backgrounds, some fittings with mutual obscuration or inconspicuous features will be hidden in complex environments and it is hard to detect them accurately. In response to this problem, this paper proposes a multi-category hardware detection model based on visual relationship masks. By mining and extracting the prior knowledge of visual relationships between power transmission line fittings that contain spatial information, the visual relationship mask(VRM), and visual relationship detection network(VRDN) are constructed, and the prior knowledge is integrated into the visual relationship module as auxiliary information, realizing the precise positioning and recognition of multi-category fittings. A variety of qualitative and quantitative experiments have been performed on a data set with 14 types of hardware. The results show that the average detection accuracy of the improved model can be increased to 76.25%, and the detection effect is better than other advanced target detection models.
References:
[1] 王宾, 倪江, 王海港, 等. 输电线路弧光高阻接地故障单端测距分析[J]. 中国电机工程学报, 2017, 37(5): 1333–1341
WANG Bin, NI Jiang, WANG Haigang, et al. Single-end fault location for high impedance arc grounding fault in transmission line[J]. Proceedings of the CSEE, 2017, 37(5): 1333–1341
[2] 刘传洋, 吴一全. 基于深度学习的输电线路视觉检测方法研究进展[J/OL]. 中国电机工程学报, 2022: 1?24. (2022?08?31). https://kns.cnki.net/kcms/detail/11.2107.TM.20220831.1053.002.html.
LIU Chuanyang, WU Yiquan. Research progress of vision detection methods based on deep learning for transmission lines[J/OL]. Proceedings of the CSEE, 2022: 1?24. (2022?08?31).https://kns.cnki.net/kcms/detail/11.2107.TM.20220831.1053.002.html.
[3] 马忠梅, 韩宝卿, 李文娟, 等. 智能识别技术在无人机输电线路巡检中的应用[J]. 集成电路应用, 2022, 39(8): 126–127
MA Zhongmei, HAN Baoqing, LI Wenjuan, et al. Application of intelligent identification in UAV inspection for transmission lines[J]. Application of IC, 2022, 39(8): 126–127
[4] 王杨杨, 曹晖, 莫文昊. 基于深度学习的改进型YOLOv4输电线路鸟巢检测与识别[J]. 智慧电力, 2023, 51(1): 101–107
WANG Yangyang, CAO Hui, MO Wenhao. Bird’s nest detection and identification on improved YOLOv4 transmission line based on deep learning[J]. Smart power, 2023, 51(1): 101–107
[5] 李轶. 基于深度学习的无人机输电线路巡检故障检测研究[J]. 流体测量与控制, 2022, 3(6): 56–58,82
LI Yi. Research on fault detection of UAV transmission line inspection based on deep learning[J]. Fluid measurement & control, 2022, 3(6): 56–58,82
[6] 翟永杰, 杨旭, 赵振兵, 等. 融合共现推理的Faster R-CNN输电线路金具检测[J]. 智能系统学报, 2021, 16(2): 237–246
ZHAI Yongjie, YANG Xu, ZHAO Zhenbing, et al. Integrating co-occurrence reasoning for Faster R-CNN transmission line fitting detection[J]. CAAI transactions on intelligent systems, 2021, 16(2): 237–246
[7] 和敬涵, 罗国敏, 程梦晓, 等. 新一代人工智能在电力系统故障分析及定位中的研究综述[J]. 中国电机工程学报, 2020, 40(17): 5506–5516
HE Jinghan, LUO Guomin, CHENG Mengxiao, et al. A research review on application of artificial intelligence in power system fault analysis and location[J]. Proceedings of the CSEE, 2020, 40(17): 5506–5516
[8] 刘召, 张黎明, 耿美晓, 等. 基于改进的Faster R-CNN高压线缆目标检测方法[J]. 智能系统学报, 2019, 14(4): 627–634
LIU Zhao, ZHANG Liming, GENG Meixiao, et al. Object detection of high-voltage cable based on improved Faster R-CNN[J]. CAAI transactions on intelligent systems, 2019, 14(4): 627–634
[9] 宋万潼, 李冰锋, 费树岷. 基于先验知识的航拍绝缘子检测方法[J]. 计算机工程, 2021, 47(8): 301–307,314
SONG Wantong, LI Bingfeng, FEI Shumin. Prior knowledge-based insulator detection method using aerial images[J]. Computer engineering, 2021, 47(8): 301–307,314
[10] 汤踊, 韩军, 魏文力, 等. 深度学习在输电线路中部件识别与缺陷检测的研究[J]. 电子测量技术, 2018, 41(6): 60–65
TANG Yong, HAN Jun, WEI Wenli, et al. Research on part recognition and defect detection of trainsmission line in deep learning[J]. Electronic measurement technology, 2018, 41(6): 60–65
[11] 高强, 廉启旺. 航拍图像中绝缘子目标检测的研究[J]. 电测与仪表, 2019, 56(5): 119–123
GAO Qiang, LIAN Qiwang. Research on target detection of insulator in aerial image[J]. Electrical measurement & instrumentation, 2019, 56(5): 119–123
[12] 赵振兵, 江爱雪, 戚银城, 等. 嵌入遮挡关系模块的SSD模型的输电线路图像金具检测[J]. 智能系统学报, 2020, 15(4): 656–662
ZHAO Zhenbing, JIANG Aixue, QI Yincheng, et al. Fittings detection in transmission line images with SSD model embedded occlusion relation module[J]. CAAI transactions on intelligent systems, 2020, 15(4): 656–662
[13] 戚银城, 江爱雪, 赵振兵, 等. 基于改进SSD模型的输电线路巡检图像金具检测方法[J]. 电测与仪表, 2019, 56(22): 7–12,43
QI Yincheng, JIANG Aixue, ZHAO Zhenbing, et al. Fittings detection method in patrol images of transmission line based on improved SSD[J]. Electrical measurement & instrumentation, 2019, 56(22): 7–12,43
[14] WAN Neng, TANG Xuming, LIU Siyan, et al. Transmission line image object detection method considering fine-grained contexts[C]//2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference. Chongqing: IEEE, 2020: 499?502.
[15] 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
[16] JOYCE J M. Kullback-leibler divergence[M]//International encyclopedia of statistical science. Berlin: Springer, 2011: 720?722.
[17] SENHAJI K, RAMCHOUN H, ETTAOUIL M. Training feedforward neural network via multiobjective optimization model using non-smooth L1/2[J]. Neurocomputing, 2020, 410: 1–11.
[18] FAN Qi, ZHOU Wei, TANG Chikeung, et al. Few-shot object detection with attention-RPN and multi-relation detector[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Seattle: IEEE, 2020: 4013?4022.
[19] 刘兴亚, 周洋. 特高压交流输电线路跳线安装施工技术[J]. 集成电路应用, 2021, 38(12): 94–95
LIU Xingya, ZHOU Yang. Installation and construction technology of jumper for UHV AC transmission line[J]. Application of IC, 2021, 38(12): 94–95
[20] HU Jie, SHEN Li, SUN Gang. Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7132?7141.
[21] BOTTOU L. Stochastic gradient descent tricks[J]. Neural networks: tricks of the trade (second edition), 2012: 421?436.
[22] 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.
[23] LIN T Y, DOLLáR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 936?944.
[24] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: common objects in context[M]//Computer Vision – ECCV 2014. Cham: Springer International Publishing, 2014: 740?755.
[25] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]//European Conference on Computer Vision. Cham: Springer, 2016: 21?37.
[26] REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL]. (2018?04?08)[2022?02?24]. https://arxiv.org/abs/1804.02767.
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