Image processing and computer vision algorithms are applied to inspect defects in railways for safety and maintenance, which is called image-based railway inspection system (IRIS).
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For that reason, the proposed system can provide various advantages such as cost reduction for maintenance and accident prevention.
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In experiments, we demonstrate that the proposed system accurately finds facilities and detects their potential defects. The proposed system consists of three main modules: (i) image reconstruction for registration of facility positions, (ii) facility detection using an improved single shot detector, and (iii) deformed region detection using image processing and computer vision techniques. Unlike an area-based camera, the line scan camera quickly acquires images with a wide field of view. We installed line scan camera on the roof of the train. The proposed system aims to automatically detect wears and cracks by comparing a pair of corresponding image sets acquired at different times. In this paper, we present a novel railway inspection system using facility detection based on deep convolutional neural network and computer vision-based image comparison approach. Although various automatic approaches were proposed using image processing and computer vision techniques, most of them are focused only on railway tracks.
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#Railway Track Crack Detection System Project Ppt manual
For sustainable operation and maintenance of urban railway infrastructure, intelligent visual inspection of the railway infrastructure attracts increasing attention to avoid unreliable, manual observation by humans at night, while trains do not operate.