面向绝缘手套佩戴状况检测的小目标检测与匹配算法  

Small Target Detection and Matching Algorithm for Wearing Condition Detection of Insulating Gloves

作　　者：郑海洋 宋纯贺[1,2,3,4,5] 武婷婷 刘硕 周忠冉 ZHENG Hai-yang;SONG Chun-he;WU Ting-ting;LIU Shuo;ZHOU Zhong-ran(State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;Key Laboratory of Networked Control Systems,Chinese Academy of Sciences,Shenyang 110016,China;Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110169,China;University of Chinese Academy of Sciences,Beijing 100049,China;Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;Nanjing NARI Information&Communication Technology Co.,Ltd.,Nanjing 210006,China)

机构地区：[1]中国科学院沈阳自动化研究所机器人学国家重点实验室,沈阳110016 [2]中国科学院网络化控制系统重点实验室,沈阳110016 [3]中国科学院机器人与智能制造创新研究院,沈阳110169 [4]中国科学院大学,北京100049 [5]中国科学院沈阳自动化研究所,沈阳110016 [6]南京南瑞信息通信科技有限公司,南京210006

出　　处：《小型微型计算机系统》2023年第9期1989-1995,共7页Journal of Chinese Computer Systems

基　　金：江苏省重点研发计划项目(BE2020001-02)资助.

摘　　要：基于图像识别的变电站现场人员不安全行为监测对于保障电力生产安全具有重要的意义.对未穿戴绝缘手套的识别是安全监测的重要内容,但手部/手套面积小、有效样本数量少、多人场景中未佩戴手套人员难以识别等问题严重制约了识别算法的性能.针对上述问题,本文提出了一种面向绝缘手套佩戴状况检测的小目标检测与匹配算法.首先,针对数据集中有效样本数量少且特征数量严重不平衡的问题,采用颜色变换与图像拉伸等方法对数据集的图片进行数据增广.其次,针对手部/手套面积小导致目标识别率不高的问题,提出一种基于改进YOLOv3网络的检测算法.一方面,对原网络中的特征金字塔结构进行改进,对网络中多层级特征信息进行融合,提升小目标识别精度;另一方面,使用K均值算法分析数据集,获得适合本数据集的初始候选框,进一步提升小目标识别性能.最终,针对目前算法中仅进行手部/手套识别,但在多人场景中难以识别对应的人员的问题,设计了一套手部与人体的关联匹配算法,可以有效的对检测结果进行匹配.实验结果表明:本文提出的算法能够对绝缘手套佩戴情况进行有效地检测,改进的YOLOv3模型的准确率提升了29%,并且经过改进的YOLOv3+分配算法模型的准确率提升了33.43%.The monitoring of unsafe behavior of on-site personnel in substations based on image recognition is of great significance for ensuring the safety of power production.The identification of non-insulated gloves is an important part of safety monitoring,but the small hand/glove area,the small number of valid samples,and the difficulty in identifying people without gloves in multi-person scenarios seriously restrict the performance of the recognition algorithm.Aiming at the above problems,this paper proposes a small target detection and matching algorithm for the detection of the wearing condition of insulating gloves.First,in view of the small number of valid samples in the dataset and the serious imbalance in the number of features,methods such as color transformation and image stretching are used to augment the images in the dataset.Secondly,in view of the problem that the target recognition rate is not high due to the small area of??the hand/glove,a detection algorithm based on the improved YOLOv3 network is proposed.On the one hand,the feature pyramid structure in the original network is improved,and the multi-level feature information in the network is fused to improve the accuracy of small target recognition;on the other hand,the K-means algorithm is used to analyze the data set to obtain initial candidates suitable for this data set,and further improve the performance of small target recognition.Finally,in view of the problem that the current algorithm only performs recognition without gloves,but it is difficult to identify the corresponding person in a multi-person scene,a set of hand and human body correlation matching algorithms are designed,which can effectively match the detection results.The experimental results show that the algorithm proposed in this paper can effectively detect the wearing of insulating gloves,the accuracy of the improved YOLOv3 model is increased by 29%,and the accuracy of the improved YOLOv3+allocation algorithm model is increased by 33.43%.

关 键 词：绝缘手套 小目标检测 特征金字塔 K均值算法 关联匹配 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]