Deep Convolutional Neural Networks for Rail Surface Defect Perception

Rail surface defect inspection is a vital element for ensuring railway safety. We critically analyze the application of deep convolutional neural networks (CNN) in Rail Surface Defect Perception (RSDP). We scrutinize 43 studies, revealing how CNN, YOLO, R-CNN, and Semantic Segmentation techniques, have revolutionized RSDP by surpassing traditional inspection methods in speed, accuracy, and efficiency. Our examination underscores the necessity for comprehensive and standardized datasets to support the varied and complex nature of rail surface defects. Additionally, we highlight the persistent challenges such as limited defect samples and the imperative for real-time detection capabilities. The paper details the performance and adaptation of various CNN models for RSDP, charting a course for future inquiry. We advocate for enhanced datasets, unified defect classification, and refined deep learning models, aiming to bolster the progress in CNN-driven RSDP technology for enhanced railway safety.