Infrared Fault Detection Method for Dense Electrolytic Bath Polar Plate Based on YOLOv5s  

作　　者：Huiling Yu Yanqiu Hang Shen Shi Kangning Wu Yizhuo Zhang 

机构地区：[1]Software Engineering,Department of Computer Science,Changzhou University,Changzhou,213146,China [2]Electrical Engineering,Department of Computer Science,Changzhou University,Changzhou,213146,China

出　　处：《Computers, Materials & Continua》2024年第9期4859-4874,共16页计算机、材料和连续体（英文）

摘　　要：Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal production.Aiming at the problems of time-consuming and poor accuracy of existing infrared methods for high-temperature detection of dense pole plates in electrolysis tanks,an infrared dense pole plate anomalous target detection network YOLOv5-RMF based on You Only Look Once version 5(YOLOv5)is proposed.Firstly,we modified the Real-Time Enhanced Super-Resolution Generative Adversarial Network(Real-ESRGAN)by changing the U-shaped network(U-Net)to Attention U-Net,to preprocess the images;secondly,we propose a new Focus module that introduces the Marr operator,which can provide more boundary information for the network;again,because Complete Intersection over Union(CIOU)cannot accommodate target borders that are increasing and decreasing,replace CIOU with Extended Intersection over Union(EIOU),while the loss function is changed to Focal and Efficient IOU(Focal-EIOU)due to the different difficulty of sample detection.On the homemade dataset,the precision of our method is 94%,the recall is 70.8%,and the map@.5 is 83.6%,which is an improvement of 1.3%in precision,9.7%in recall,and 7%in map@.5 over the original network.The algorithm can meet the needs of electrolysis tank pole plate abnormal temperature detection,which can lay a technical foundation for improving production efficiency and reducing production waste.

关 键 词：Infrared polar plate fault detection YOLOv5 Real-ESRGAN Marr boundary detection operator Focal-EIoU loss 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]