Detection of Component Types and Track Damage for High-Speed Railway Using Region-Based Convolutional Neural Networks

Abstract

High-speed railway plays critical roles in public safety and the country’s economy. Visual detection of components and damages can reflect the health conditions of high-speed railway. Human-based visual inspection is a difficult and time-consuming task and its detection results significantly rely on subjective judgement of human inspectors. Image-based detection methods abandon the weakness of human-based visual inspection. However, in practice, the complex real-world situations, such as lighting and shadow changes, can lead to challenges to the wide adaptability of image process techniques. To overcome these challenges, this paper provides a Faster Region-based Convolutional Neural Network (Faster R-CNN)-based detection method of component types and track damage for high-speed railway. To realize the method, a database including 575 images labeled for three component types and one track damage type of high-speed railway is built. A Faster R-CNN architecture based on ZF-Net is modified, then trained and validated using the built database. The performance of the trained Faster R-CNN is evaluated using 50 new images which are not be used for training process. The results show that the proposed method can indeed detect the component types and track damage for high-speed railway.