[1]ZHANG Mingquan,XING Fude,LIU Dong.External defect detection of transformer substation equipment based on improved Faster R-CNN[J].CAAI Transactions on Intelligent Systems,2024,19(2):290-298.[doi:10.11992/tis.202207016]
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External defect detection of transformer substation equipment based on improved Faster R-CNN

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 2 Page number: 290-298 Column: 学术论文—机器学习 Public date: 2024-03-05
Title:
External defect detection of transformer substation equipment based on improved Faster R-CNN
Author(s):
ZHANG Mingquan;  XING Fude;  LIU Dong
Computer Department, North China Electric Power University, Baoding 071003, China
Keywords:
external defects of transformer substation equipment; deep learning; object detection; convolutional neural network; Faster R-CNN; feature extraction; feature fusion pyra-mid structure; loss function
CLC:
TP391.4
DOI:
10.11992/tis.202207016
Abstract:
There are challenges in object detection on external defects of transformer substation equipment, such as various target shapes, complex surrounding environment, low recognition accuracy of current representative algorithms, and severe false or missed detection. By comparing the detection results of different object detection algorithms on the transformer substation equipment defect data set, it is revealed that the faster R-CNN algorithm with the feature fusion pyramid structure has higher detection accuracy. However, there are still opportunities to improve the detection accuracy of small target objects and equipment leakage. Thus, in this study, an enhanced, faster R-CNN-based algorithm is developed. It improves the detection accuracy of defects by enhancing the input image data, adding the spatial pyramid pooling structure to the network to improve the feature fusion method, and thereby boosting the classification and bounding box regression loss function. Compared with the original faster R-CNN, the experimental findings demonstrate that the improved algorithm has increased AP (0.5 : 0.95) (average precision) by 2.7% and AP (0.5) by 4.3% in the detection results of the transformer substation equipment with respect to the defect object detection data set and the detection accuracy of small target objects has also been improved by 1.8%. This work confirms the effectiveness of the method proposed here.
References:
[1] 薛艺为, 孙奇珍, 党卫军. 基于Faster RCNN的配网设备红外图像缺陷识别方法[J]. 信息技术, 2020, 44(7): 79–83,91
XUE Yiwei, SUN Qizhen, DANG Weijun. Infrared image defect recognition method of distribution network equipment based on Faster RCNN[J]. Information technology, 2020, 44(7): 79–83,91
[2] 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. Infrared image defect detection of power equipment[J]. Electric power, 2021, 54(2): 147–155
[3] 徐小冰, 袁婧, 廖雁群, 等. 基于Faster RCNN与Mean-Shift的电缆附件缺陷红外图像自动诊断方法[J]. 高电压技术, 2020, 46(9): 3070–3080
XU Xiaobing YUAN Jing LIAO Yanqun, et al. Autonomous diagnosis method for defects of cable accessories based on Faster RCNN and mean-shift algorithm by infrared images[J]. High voltage engineering, 2020, 46(9): 3070–3080
[4] 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
[5] 赵文清, 程幸福, 赵振兵, 等. 注意力机制和Faster RCNN相结合的绝缘子识别[J]. 智能系统学报, 2020, 15(1): 92–98
ZHAO Wenqing, CHENG Xingfu, ZHAO Zhenbing, et al. Insulator recognition based on attention mechanism and Faster RCNN[J]. CAAI transactions on intelligent systems, 2020, 15(1): 92–98
[6] 冯晗, 姜勇. 使用改进 Yolov5 的变电站绝缘子串检测方法[J]. 智能系统学报, 2022, 18(2): 325–332
FENG Han JIANG Yong. A substation insulator string detection method based on an improved Yolov5[J]. CAAI transactions on intelligent systems, 2022, 18(2): 325–332
[7] 刘开培, 李博强, 秦亮, 等. 深度学习目标检测算法在架空输电线路绝缘子缺陷检测中的应用研究综述[J]. 高电压技术, 2023, 49(9): 3584–3595
LIU Kaipei, LI Boqiang, QIN Liang, et al. Review of application research of deep learning object detection algorithms in insulator defect detection of overhead transmission lines[J]. High voltage engineering, 2023, 49(9): 3584–3595
[8] 刘黎, 韩睿, 韩译锋, 等. 改进的Faster-RCNN目标检测方法在变电站悬挂异物检测中的应用[J]. 电测与仪表, 2021, 58(1): 142–146
LIU Li, HAN Rui, HAN Yifeng, et al. Application of an improved Faster-RCNN object detection method in the detection of suspended foreign matters in substation[J]. Electrical measurement & instrumentation, 2021, 58(1): 142–146
[9] 戚银城, 赵席彬, 耿劭锋, 等. 基于遮挡关系推理的输电线路图像金具检测[J]. 智能系统学报, 2022, 17(6): 1154–1162
QI Yincheng, ZHAO Xibin, GENG Shaofeng, et al. Fittings detection in transmission line images with occlusion relation inference[J]. CAAI transactions on intelligent systems, 2022, 17(6): 1154–1162
[10] 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
[11] 翟永杰, 王乾铭, 杨旭, 等. 融合外部知识的输电线路多金具解耦检测方法[J]. 智能系统学报, 2022, 17(5): 980–989
ZHAI Yongjie, WANG Qianming, YANG Xu, et al. A multi-fitting decoupling detection method for transmission lines based on external knowledge[J]. CAAI transactions on intelligent systems, 2022, 17(5): 980–989
[12] 琚泽立, 邢伟, 金鸿鹏, 等. 基于轻量化网络的变电站缺陷图片检测算法[J]. 电网与清洁能源, 2020, 36(8): 43–49
JU Zeli, XING Wei, JIN Hongpeng, et al. Detection of substation faults image based on lightweight network[J]. Power system and clean energy, 2020, 36(8): 43–49
[13] 万吉林, 吴国强, 管敏渊, 等. 基于RetinaNet的变压器图像小部件智能识别方法[J]. 电力系统保护与控制, 2021, 49(12): 166–173
WAN Jilin, WU Guoqiang, GUAN Minyuan, et al. Intelligent recognition method for transformer small components based on RetinaNet[J]. Power system protection and control, 2021, 49(12): 166–173
[14] 尹子会, 孟荣, 范晓丹, 等. 融合边缘计算和改进Faster R-CNN的变电站设备典型视觉缺陷检测系统[J]. 中国科技论文, 2021, 16(3): 343–348
YIN Zihui, MENG Rong, FAN Xiaodan, et al. Typical visual defect detection system of substation equipment based on edge computing and improved Faster R-CNN[J]. China sciencepaper, 2021, 16(3): 343–348
[15] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014: 580-587.
[16] GIRSHICK R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision . Santiago: IEEE, 2016: 1440-1448.
[17] REN S, HE K, 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.
[18] LIN T Y, DOLL?R 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.
[19] 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 . Las Vegas: IEEE, 2016: 779-788.
[20] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu: IEEE, 2017: 6517-6525.
[21] REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08)[2020-01-01]. https://arxiv.org/abs/1804.02767.
[22] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. (2020-04-23)[2022-01-01]. https://arxiv.org/abs/2004.10934.pdf.
[23] LIU W, ANGUELOV D, ERHAN D, et al. (2016). Ssd: Single shot multibox detector[EB/OL]. (2016-03-30)[2022-01-01]. https://arxiv.org/abs/1512.02325.
[24] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//2017 IEEE International Conference on Computer Vision . Venice: IEEE, 2017: 2999-3007.
[25] LIU Shu, QI Lu, QIN Haifang, et al. Path aggregation network for instance segmentation[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 8759-8768.
[26] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, 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.
[27] YU Jiahui, JIANG Yuning, WANG Zhangyang, et al. UnitBox: an advanced object detection network[C]//Proceedings of the 24th ACM International Conference on Multimedia. Amsterdam The Netherlands. New York: ACM, 2016: 516-520.
[28] REZATOFIGHI H, TSOI N, GWAK J, et al. Generalized intersection over union: a metric and a loss for bounding box regression[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach: IEEE, 2020: 658-666.
[29] ZHENG Zhaohui, WANG Ping, LIU Wei, et al. Distance-IoU loss: faster and better learning for bounding box regression[J]. Proceedings of the AAAI conference on artificial intelligence, 2020, 34(7): 12993–13000.
[30] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: common objects in context[C]//European Conference on Computer Vision. Cham: Springer, 2014: 740-755.
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