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计算机系统应用:2020,29(8):158-164
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基于改进Mask RCNN的电力检修违规操作检测
(1.国网山东省电力公司, 济南 250001;2.山东鲁能软件技术有限公司, 济南 250001;3.中国石油大学(华东) 计算机科学与技术学院, 青岛 266580)
Illegal Operation Detection in Electric Maintenance Based on Improved Mask RCNN
(1.State Grid Shandong Electric Power Company, Jinan 250001, China;2.Shandong Luneng Software Technology Co.Ltd, Jinan 250001, China;3.College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China)
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投稿时间:2020-01-08    修订日期:2020-02-08
中文摘要: 电力检修现场中施工行为的规范关系到工作人员的人身安全, 对电力行业的发展至关重要. 为了从计算机视觉的角度对电力检修工作人员的违规操作行为进行检测, 基于Mask RCNN算法设计了一种多任务多分支违规行为检测算法, 综合目标检测、关键点检测与实例分割任务, 并行对目标进行检测并获得目标的边框坐标、关键点与mask信息. 实验结果表明, 相对于原来的Mask RCNN, 该算法在实例分割和关键点检测方面都有了显著的提升, 具有更高的准确性和鲁棒性, 在电力检修违规检测方面满足实际部署的精度要求.
Abstract:The norm of opereation in electric power maintenance is related to the personal safety of the staff, and is very im-portant to the development of electric power industry. In order to detect the illegal operation behavior of power maintenance workers from the perspective of computer vision, a multi-tasking and multi-branch illegal behavior detection algorithm was designed based on the Mask RCNN algorithm. It integrates target detection, key point detection and instance segmentation tasks, and performs parallel target detection. Detect and obtain the frame coordinates, keypoints, and mask information of the target. The experimental result demonstrates that this algorithm has significantly improved the precision in instance segmentation and key point detection, has higher accuracy and robustness compared with Mask RCNN. And it meets the accuracy requirements of actual deployment in power maintenance violation detection.
文章编号:7559     中图分类号:    文献标志码:
基金项目:国家重点研发计划(2017ZX05013-002); 山东省自然基金(ZR2019MF049)
引用文本:
沈茂东,周伟,宋晓东,裴健,邓昊,马超,房凯.基于改进Mask RCNN的电力检修违规操作检测.计算机系统应用,2020,29(8):158-164
SHEN Mao-Dong,ZHOU Wei,SONG Xiao-Dong,PEI Jian,DENG Hao,MA Chao,FANG Kai.Illegal Operation Detection in Electric Maintenance Based on Improved Mask RCNN.COMPUTER SYSTEMS APPLICATIONS,2020,29(8):158-164

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