A Substation Image Inspection Method Based on Visual Communication and Combination of Normal and Abnormal Samples

To address the issue of missed detection of abnormal images caused by scarcity of defect samples and inadequate model training that characterize the current substation image inspection methods, this paper proposes a new substation image inspection method based on visual communication and combination of normal and abnormal samples. In this new method, the quality of substation equipment images is first evaluated, and images are recaptured when they are defocused and underexposed. Images are then preprocessed to eliminate the impact of noise on the algorithm. Image feature alignment is then performed to mitigate camera displacement errors that could degrade algorithmic accuracy. Subsequently, normal-labeled images are used to train the model, and a normal sample database is thus established. Built upon visual communication infrastructure with low-level quantization, the visual feature discrepancy between the current inspection images and those in the normal sample database is calculated using the Learned Perceptual Image Patch Similarity (LPIPS) metric. Through this process, the normal images are filtered out while abnormal images are classified and reported. Finally, this new method is validated at a municipal power supply company in China. When the abnormal image reporting rate is 18.9%, the abnormal image reporting accuracy rate is 100%. This demonstrates that the proposed method can significantly decrease the workload of substation operation and maintenance personnel in reviewing substation inspection images, reduce the time required for a single inspection of substation equipment, and improve the efficiency of video-based substation inspections.