[1]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.[doi:10.11992/tis.202108036]
Copy
Fittings detection in transmission line images with occlusion relation inference
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
17
Number of periods:
2022 6
Page number:
1154-1162
Column:
学术论文—机器感知与模式识别
Public date:
2022-11-05
- Title:
- Fittings detection in transmission line images with occlusion relation inference
- Keywords:
- transmission line; fittings; occlusion relationship description; structure inference; Faster R-CNN; object detection; gated recurrent unit; graph
- CLC:
- TP18; TM726
- DOI:
- 10.11992/tis.202108036
- Abstract:
- The accurate detection of typical fittings in transmission line images is the premise of fault detection. This study proposes a typical fittings detection method that combines occlusion structure information and scene association information to address the problems of low detection accuracy and missed detection of the common target detection model, such as dense distribution and serious occlusion of the fittings. Based on the classical Faster R-CNN detection model, the method extracts features of the entire image of fittings as scene association information, learns the intersecting area information between the marking frames as occlusion structure information, uses a graph to model the feature of fittings, scene-related information and occlusion structure information, and constructs a structure reasoning module through the information transmission mechanism of the gated recirculating unit to complete the joint inference detection of the category and position of fittings. Experiments with eight types of fittings with occlusion relationships are chosen to validate the effectiveness of the proposed method. The Faster R-CNN model shows an mAP value of 81.30%, while the proposed model has an mAP value of 84.15%. The experiments show that the proposed method can improve the detection accuracy of serious occlusion fittings to some extent and that it has laid a good foundation for the subsequent fault diagnosis of the fittings.
- References:
-
[1] 赵强, 左石. 输电线路金具理论与应用[M]. 北京: 中国电力出版社, 2013: 2-12.
[2] 苏奕辉, 梁伟放. 架空输电线路隐患、缺陷及故障表象辨识图册[M]. 北京: 中国电力出版社, 2017.
[3] DENG Chuang, WANG Shengwei, HUANG Zhi, et al. Unmanned aerial vehicles for power line inspection: a cooperative way in platforms and communications[J]. Journal of communications, 2014, 9(9): 687–692.
[4] TONG Weiguo, YUAN Jinsha, LI Baoshu. Application of image processing in patrol inspection of overhead transmission line by helicopter[J]. Power system technology, 2010, 34(12): 204–208.
[5] 胡彩石, 吴功平, 曹珩, 等. 高压输电线路巡线机器人障碍物视觉检测识别研究[J]. 传感技术学报, 2008, 21(12): 2092–2096
HU Caishi, WU Gongping, CAO Heng, et al. Research of obstacle recognition based on vision for high voltage transmission line inspection robot[J]. Chinese journal of sensors and actuators, 2008, 21(12): 2092–2096
[6] 付晶, 邵瑰玮, 吴亮, 等. 利用层次模型进行训练学习的线路设备缺陷检测方法[J]. 高电压技术, 2017, 43(1): 266–275
FU Jing, SHAO Guiwei, WU Liang, et al. Defect detection of line facility using hierarchical model with learning algorithm[J]. High voltage engineering, 2017, 43(1): 266–275
[7] YAN Shujia, JIN Lijun, DUAN Shaohui, et al. Power line image segmentation and extra matter recognition based on improved Otsu algorithm[C]//2nd International Conference on Electric Power Equipment-Switching Technology. Shenzhen: China, 2013: 1-4.
[8] 宋伟, 左丹, 邓邦飞, 等. 高压输电线防震锤锈蚀缺陷检测[J]. 仪器仪表学报, 2016, 37(S1): 113–117
SONG Wei, ZUO Dan, DENG Bangfei, et al. Corrosion defect detection of earthquake hammer for high voltage transmission line[J]. Chinese journal of scientific instrument, 2016, 37(S1): 113–117
[9] 杨罡, 孙昌雯, 张娜, 等. 基于多尺度特征融合的输电线路关键部件检测[J]. 电测与仪表, 2020, 57(3): 54–59
YANG Gang, SUN Changwen, ZHANG Na, et al. Detection of key components of transmission lines based on multi-scale feature fusion[J]. Electrical measurement & instrumentation, 2020, 57(3): 54–59
[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] 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
[12] HU Han, GU Jiayuan, ZHANG Zheng, et al. Relation networks for object detection[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 3588-3597.
[13] LIU Yong, WANG Ruiping, SHAN Shiguang, et al. Structure inference net: object detection using scene-level context and instance-level relationships[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018 : 6985-6994.
[14] 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(06): 60–65
TANG Yue, HAN Jun, WEI Wenli, et al. Research on component identification and defect detection in power transmission lines using deep learning[J]. Electronic measurement technology, 2018, 41(06): 60–65
[15] 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.
[16] GIRSHICK R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 1440-1448.
[17] JIANG Borui, LUO Ruixuan, MAO Jiayuan, et al. Acquisition of localization confidence for accurate object detection[C]//Proceedings of the European Conference on Computer Vision (ECCV). Munich: Springer Cham, 2018: 816-832.
[18] XU Danfei, ZHU Yuke, CHOY C B, et al. Scene graph generation by iterative message passing[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 3097-3106.
[19] TANG Duyu, QIN Bing, LIU Ting. Document modeling with gated recurrent neural network for sentiment classification[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon: ACL, 2015: 1422-1432.
[20] EVERINGHAM M, ESLAMI S M A, GOOL L, et al. The pascal visual object classes challenge: a retrospective[J]. International journal of computer vision, 2015, 111(1): 98–136.
[21] 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. Defect detection and recognition of power grid inspection images based on deep learning[J]. Power system protection and control, 2021, 49(5): 91–97
[22] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam: Springer Cham, 2016: 21-37.
[23] SONG Yifeng, WANG Lin, JIANG Yong, et al. A vision-based method for the broken spacer detection[C]//2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems. Shenyang: IEEE, 2015: 715-719.
[24] 戚银城, 武学良, 赵振兵, 等. 嵌入双注意力机制的Faster R-CNN航拍输电线路螺栓缺陷检测[J]. 中国图象图形学报, 2021, 26(11): 2594–2604
QI Yincheng, WU Xueliang, ZHAO Zhenbing, et al. Faster R-CNN aerial photography transmission line bolt defect detection with embedded dual attention mechanism[J]. Chinese journal of image and graphics, 2021, 26(11): 2594–2604
[25] WANG Hongchao, SHAO Yunfeng, ZOU Suli, et al. Detection of cotter pins missing of connection fittings on transmission lines of power system[C]//2021 40th Chinese Control Conference. Shanghai: IEEE, 2021: 6873-6879.
- Similar References:
Memo
-
Last Update:
1900-01-01