FaultNet: Faulty Rail-Valves Detection Using Deep Learning and Computer Vision

Abstract

Regular inspection of rail valves and engines is an important task to ensure safety and efficiency of railway networks around the globe. Over the past decade, computer vision and pattern recognition based techniques have gained traction for these inspection and defect detection tasks. An end-to-end trained system can potentially provide a low-cost, high throughput, and cheap alternative to manual visual inspection of such components. However, such systems require huge amount of defective images for networks to understand complex defects. In this paper, a multi-phase deep learning based technique is proposed to perform accurate fault detection of rail-valves. Our approach uses a two-step method to perform high precision image segmentation of rail-valves resulting in pixel-wise accurate segmentation. Thereafter, a computer vision technique is used to identify faulty valves. We demonstrate that the proposed approach results in improved detection performance when compared to current state-of-the-art techniques used in fault detection.