Visual Positioning Detection of EMU Brake Pad Based on Deep Learning

［Objective］With the continuous development of the rail transit industry, its operation scale and safety requirements are constantly increasing. To address the issues of low efficiency, missed and false detections in the manual inspection of EMU (electric multiple unit) brake pads, it is necessary to study visual positioning detection methods for EMU brake pads. ［Method］A visual positioning detection method for EMU brake pads based on deep learning is proposed. To address the issue of indistinct image features in real-world scenarios, based on the Faster R-CNN (region conventional neural network) algorithm, an edge detection branch is introduced, and a target edge loss function is added to the loss function, integrating edge information from an auxiliary network. Bilinear interpolation is used to calculate feature pixel values, preserving more feature information of the EMU brake pads. ［Result & Conclusion］The improved Faster R-CNN model proposed here can handle details at the edges, accelerate network convergence, and learn more edge features of the EMU brake pads. Through the bilinear interpolation method, the misalignment errors of target features during ROI (region of interest) pooling quantization process are reduced. The proposed brake pad detection method achieves an average precision of 98.42% and an FPS (frames per second) of 27.77%.