[1]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.[doi:10.11992/tis.202001008]
 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.[doi:10.11992/tis.202001008]
点击复制

嵌入遮挡关系模块的SSD模型的输电线路图像金具检测(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年4期
页码:
656-662
栏目:
学术论文—智能系统
出版日期:
2020-10-30

文章信息/Info

Title:
Fittings detection in transmission line images with SSD model embedded occlusion relation module
作者:
赵振兵1 江爱雪1 戚银城1 张薇1 赵文清2
1. 华北电力大学 电气与电子工程学院,河北 保定 071003;
2. 华北电力大学 控制与计算机工程学院,河北 保定 071003
Author(s):
ZHAO Zhenbing1 JIANG Aixue1 QI Yincheng1 ZHANG Wei1 ZHAO Wenqing2
1. School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China;
2. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
关键词:
输电线路金具遮挡度遮挡关系描述遮挡关系模块SSD标注框目标检测深度学习
Keywords:
transmission line fittingsocclusionocclusion relationship descriptionocclusion relationship modulesingle shot multibox detectorgroundtruth boxobject detectiondeep learning
分类号:
TP18;TN911.73
DOI:
10.11992/tis.202001008
摘要:
为了提升深度学习目标检测模型在输电线路金具自动化检测任务中的准确率,针对金具检测数据集中金具目标标注框之间不可避免地广泛存在相交而导致金具目标检测定位不准确的问题,本文利用相交区域的相似性作为金具目标的上下文信息,提出目标间遮挡关系的描述方法,用于规则性描述图像中金具目标间的相互遮挡,设计遮挡关系模块,并将其嵌入到单次多框检测器(single shot multibox detector, SSD)模型中。为了验证嵌入遮挡关系模块的SSD模型的有效性,选择了8类目标标注框普遍存在相交的小目标金具进行实验,实验使用的金具检测数据集的训练集和测试集中金具目标数分别为6 271和1 713。实验证明,原始SSD模型的平均精度均值(mean average precision, mAP)为72.10%,嵌入遮挡关系模块的SSD模型的mAP为76.56%,性能提升了4.46%。
Abstract:
In order to improve the accuracy of the deep learning object detection model in the automatic detection of transmission fittings, aiming at the problem of inaccurate detection and location of fittings due to the inevitable extensive intersection between the groundtruth boxes of fittings in the fittings dataset, this article proposes a description method of the occlusion relation between the objects, so as to regularly describe the mutual occlusion between the objects by using the similarity of the intersection area as the context information of the fittings. The occlusion relation module is designed and embedded in the single shot multibox detector (SSD) model. In order to verify the effectiveness of the SSD model embedded with the occlusion relation module, eight kinds of small objects with intersecting groundtruth boxes are selected for experiments, and the object number of the training set and the test set of the fittings dataset used in the experiment is 6271 and 1713 respectively. The experiments show that the mean average precision (mAP) of the original SSD model is 72.10%, the mAP of the SSD model embedded in the occlusion relation module is 76.56%, and the performance is improved by 4.46%.

参考文献/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] 傅博, 姜勇, 王洪光, 等. 输电线路巡检图像智能诊断系统[J]. 智能系统学报, 2016, 11(1): 70-77
FU Bo, JIANG Yong, WANG Hongguang, et al. Intelligent diagnosis system for patrol check images of power transmission lines[J]. CAAI transactions on intelligent systems, 2016, 11(1): 70-77
[3] 王身丽, 黄力, 侯金华, 等. 基于最大熵的复合绝缘子表面水珠图像分割算法研究[J]. 通信电源技术, 2018, 35(1): 48-50
WANG Shenli, HUANG Li, HOU Jinhua, et al. Study on segmentation method of water drops image on composite insulator based on maximum entropy[J]. Telecom power technology, 2018, 35(1): 48-50
[4] 金立军, 胡娟, 闫书佳. 基于图像的高压输电线间隔棒故障诊断方法[J]. 高电压技术, 2013, 39(5): 1040-1045
JIN Lijun, HU Juan, YAN Shujia. Method of spacer fault diagnose on transmission line based on image procession[J]. High voltage engineering, 2013, 39(5): 1040-1045
[5] 陈晓娟, 吴英石, 赵亮. 基于随机Hough变换的OPGW防震锤识别[J]. 黑龙江电力, 2010, 32(1): 1-2
CHEN Xiaojuan, WU Yingshi, ZHAO Liang. Identification of OPGW vibration damper based on random Hough transformation[J]. Heilongjiang electric power, 2010, 32(1): 1-2
[6] 江慎旺, 许廷发, 张增, 等. 基于无人机的输电网故障跳线联板识别[J]. 液晶与显示, 2016, 31(12): 1149-1155
JIANG Shenwang, XU Tingfa, ZHANG Zeng. Recognition algorithm for fault jumper connection plate of transmission network based on UAV[J]. Chinese journal of liquid crystals and displays, 2016, 31(12): 1149-1155
[7] 翟永杰, 王迪, 赵振兵. 基于目标建议与结构搜索的绝缘子识别方法[J]. 华北电力大学学报, 2016, 43(4): 66-71
ZHAI Yongjie, WANG Di, ZHAO Zhenbing. Recognition method of insulator based on object proposals and structure research[J]. Journal of North China Electric Power University, 2016, 43(4): 66-71
[8] 王森. 输电线路图像上防震锤检测算法研究[D]. 北京: 北京交通大学, 2017: 21-44.
WANG Sen. Research on algorithms of vibration damper detection on power line image[D]. Beijing: Beijing Jiaotong University, 2017: 21-44.
[9] 刘永姣. 输电线路绝缘子视觉跟踪技术应用研究[D]. 成都: 电子科技大学, 2017: 35-45.
LIU Yongjiao. Research on application of visual tracking technology of insulator for transmission line[D]. Chengdu: University of Electronic Science and Technology of China, 2017: 35-45.
[10] 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
[11] 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 transmission line in deep learning[J]. Electronic measurement technology, 2018, 41(6): 60-65
[12] 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 and instrumentation, 2019, 56(5): 119-123
[13] 赵振兵, 崔雅萍, 戚银城, 等. 基于改进的 R-FCN 航拍巡线图像中的绝缘子检测方法[J]. 计算机科学, 2019, 46(3): 159-163
ZHAO Zhenbing, CUI Yaping, QI Yincheng, et al. Detection method of insulator in aerial inspection image based on modified R-FCN[J]. Computer science, 2019, 46(3): 159-163
[14] 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
[15] 张东, 邱翔, 曹成功, 等. 结合聚合通道和复频域特征的防震锤检测算法[J]. 计算机技术与发展, 2020(2): 1-7
ZHANG Dong, QIU Xiang, CAO Chenggong, et al. Algorithm of vibration damper detection combined with aggregation channel and complex frequency domain features[J]. Computer technology and development, 2020(2): 1-7
[16] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//European Conference on ComputerVision. Amsterdam, Netherlands, 2016: 21-37.
[17] 赵文清, 周震东, 翟永杰. 基于反卷积和特征融合的SSD小目标检测算法[J]. 智能系统学报, 2020, 15(2): 310-316
ZHAO Wenqing, ZHOU Zhendong, ZHAI Yongjie. SSD small target detection algorithm based on deconvolution and feature fusion[J]. CAAI transactions on intelligent systems, 2020, 15(2): 310-316
[18] 伍鹏瑛, 张建明, 彭建, 等. 多层卷积特征的真实场景下行人检测研究[J]. 智能系统学报, 2019, 14(2): 306-315
WU Pengying, ZHANG Jianming, PENG Jian, et al. Research on pedestrian detection based on multi-layer convolution feature in real scene[J]. CAAI transactions on intelligent systems, 2019, 14(2): 306-315
[19] CHEN X, GUPTA A. Spatial memory for context reasoning in object detection[C]//Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 2017: 4086-4096.
[20] MOTTAGHI R, CHEN X, LIU X, et al. The role of context for object detection and semantic segmentation in the wild[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 891-898.
[21] LIU Y, WANG R, SHAN S, et al. Structure inference net: object detection using scene-level context and instance-level relationships[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 6985-6994.
[22] HU H, GU J, ZhANG Z, et al. Relation networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 3588-3597.
[23] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Advances in Neural Information Processing Systems. Long Beach, USA, 2017: 5998-6008.
[24] 葛园园, 许有疆, 赵帅, 等. 自动驾驶场景下小且密集的交通标志检测[J]. 智能系统学报, 2018, 13(3): 366-372
GE Yuanyuan, XU Youjiang, ZHAO Shuai, et al. Detection of small and dense traffic signs in self-driving scenarios[J]. CAAI transactions on intelligent systems, 2018, 13(3): 366-372
[25] FU C Y, LIU W, RANGA A, et al. DSSD: deconvolutional single shot detector[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii Convention Center, USA, 2017: 2881-2890.

备注/Memo

备注/Memo:
收稿日期:2020-01-06。
基金项目:国家自然科学基金项目(61871182,61773160);北京市自然科学基金项目(4192055);河北省自然科学基金项目(F2020502009);中央高校基本科研业务费专项资金项目(2018MS095,2020YJ006);模式识别国家重点实验室开放课题(201900051);国家留学基金项目(201906735011)
作者简介:赵振兵,副教授,博士,主要研究方向为电力视觉。主持国家自然科学基金等纵向课题10项;获河北省科技进步一等奖1项。以第一完成人获得国家专利授权16项。发表学术论文30余篇,出版专著2部;江爱雪,硕士研究生,主要研究方向为电力目标检测与深度学习;赵文清,教授,博士,主要研究方向为人工智与数据挖掘。发表学术论文50余篇
通讯作者:赵振兵.E-mail:zhaozhenbing@ncepu.edu.cn
更新日期/Last Update: 2020-07-25