Electrical equipment identification in infrared images based on ROI-selected CNN method

The thermographic evaluation method is widely used in substations to detect potential faults in advance. However, thousands of infrared images are expected for each substation. Almost all the data is collected, sorted and analyzed by humans. Therefore, to automate the data management and diagnosis, this paper proposes a method based on the deep convolutional neural network (CNN) for infrared image recognition of electrical equipment. Firstly, a deep CNN identification model is built based on MobileNet with initial weights in ImageNet. In addition, combined with the dataset augmentation including cropping, flipping, rotation and zoom, 3547 images of 500 kV substation equipment are used for training. Besides, a fine-tuning method is adopted to optimize the training. Finally, a fast region of interest (ROI) selection method based on hotspot sensitivity in infrared images is used to improve the identification accuracy. The original image aspect ratio is fixed throughout the process. Results demonstrate that the prediction accuracy of the proposed method reaches 97.72% in validation, and the ROI selection method improves the confidence by 8% in the test. As a result, this proposed method can promote the calculation efficiency, and has good application prospects in embedded de vices such as cameras and substation robots.