Efficient and Real-Time Railway Track Fault Classification Using CNN Integrated with Convolutional Block Attention Module

The occurrence of various types of faults on railway rail surfaces can lead to accidents such as train derailments. In this study, a new approach that produces very fast and robust results is developed by combining the convolutional neural network and the convolutional block attention module for the classification of rail faults. The dataset used in this study contains four different fault types, and experimental studies were conducted on the public rail dataset. The impact of the convolutional block attention module on the performance of the proposed approach and its contribution to the model's generalization ability are examined, and the performance of the proposed approach increases by approximately 5% compared to the performance of the proposed approach without this module. It has been demonstrated that the proposed approach can be used effectively in railway track fault diagnosis by producing fast and effective results.