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

FaultNet: Faulty Rail-Valves Detection using Deep Learning and Computer Vision
Title:
FaultNet: Faulty Rail-Valves Detection using Deep Learning and Computer Vision
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IEEE-ITSC 2019
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Publication Date:
27 October 2019
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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 such inspection and defect detection tasks. An automated end-to-end trained system can potentially provide a low-cost, high throughput, and cheap alternative to manual visual inspection of these 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.
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© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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