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

Electrolysis tanks are used to smelt metals 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.