Research on design of image processing and recognition based on deep learning

To achieve target detection and defect recognition in power inspection images, an image processing and recognition algorithm based on deep learning is proposed. This algorithm mainly adopts an improved Faster-RCNN model, combined with HSI color feature extraction, for real-time image processing and identification of various conventional faults. Firstly, the collected power inspection images are normalized, converting the RGB colors of the images to HSI colors. By traversing each pixel in HSI space, it is determined whether there is a fault in the pixel based on the color characteristics of the image. Secondly, a DenseNet network is constructed and connected to the RoIAlign layer and prediction layer. Finally, the identification of target defects is achieved through Faster-RCNN. Simulation results show that the algorithm can accurately identify fault defects with a precision of up to 94.60%, indicating a broad application prospect in power equipment defect recognition.