Deep Residual UNet Network-based Infrared Image Segmentation Method for Electrical Equipment

Infrared thermal image processing is an effective method for detecting defects in electrical equipment. Aiming at the problem of electrical equipment segmentation in infrared thermal images with a complex background, in this study we propose a deep residual UNet network for infrared thermal image segmentation. Using a deep residual network to replace VGG16 to perform feature extraction and coding for the UNet network, a deep residual series UNET network was constructed to segment electrical equipment. To validate the effectiveness of the Res-UNet network, infrared images, including current transformers and circuit breakers, were used to test the segmentation results and were compared with the traditional UNet and Deeplabv3+ networks. The networks were tested using 876 images. The experimental results show that RES18-UNET can accurately segment electrical equipment; the segmentation precision of current transformers and circuit breakers is greater than 93%, and the mean intersection over union (MIoU) is greater than 89%. Our method obtains more accurate segmentation results than UNet and Deeplabv3+, setting the basis for intelligent diagnosis of electrical faults.