基于视频的托辊异常检测方法研究  

ANOMALY DETECTION METHOD OF ROLLER BASED ON VIDEO

作　　者：李占利[1] 胡长斌 靳红梅[1] Li Zhanli;Hu Changbin;Jin Hongmei(College of Computer Science and Technology,Xi’an University of Science and Technology,Xi’an 710600,Shaanxi,China)

机构地区：[1]西安科技大学计算机科学与技术学院,陕西西安710600

出　　处：《计算机应用与软件》2024年第7期93-99,共7页Computer Applications and Software

摘　　要：针对托辊卡死、阻塞、轴承故障等托辊异常现象检测准确率低及人工检测耗时长的问题,提出基于视频的托辊异常检测方法,对运动托辊进行实时异常检测和报警。分析异常托辊产生的原因,提出基于视频的异常托辊检测方案并设计托辊异常检测网络;实验测得异常托辊检测准确率89.53%,检测速度103帧每秒;使用五个评价标准分析了方案的优劣。相比与基于声音信号的托辊异常检测方案,基于视频的检测方法降低了传感器安装和维护难度,将卡死托辊检测准确率提高至94.57%,确定异常托辊的位置,降低了工人维修时二次定位异常托辊的难度和工作量。Aimed at the problems of low detection accuracy and long manual detection time for abnormal phenomena such as stuck rollers,blockages,and bearing failures of the rollers,an image-based abnormal detection method of the idler is proposed to realize the real-time abnormal detection and early warning of the idler.We analyzed the causes of abnormal rollers,proposed a video based abnormal roller detection scheme,and designed an abnormal roller detection network.The experiments show that the abnormal roller detection accuracy rate of 89.53%,and the detection speed of 103 FPS.Five evaluation criteria and comparative experiments were used to verify the pros and cons of the C3D network abnormal roller detection scheme.Compared with the roller anomaly detection scheme based on sound signals,the detection method based on the C3D network reduces the difficulty and cost of sensor installation and maintenance,and increases the accuracy of stuck roller detection to 94.57%.

关 键 词：托辊 带式输送机 异常检测 C3D 运动特征提取 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]