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Abstract:Detecting the rail surface faults is one of the most important components of railway inspection process which should be performed periodically. Today, the railway inspection process is commonly performed using computer vision. Performing railway inspection based on image processing can lead to false-positive results. The fact that the oil and dust residues occurring on railway surfaces can be detected as an error by the image processing software can lead to loss of time and additional costs in the railway maintenance process. In this study, a hardware and software architecture are presented to perform railway surface inspection using 3D laser cameras. The use of 3D laser cameras in railway inspection process provides high accuracy rates in real time. The reading rate of laser cameras to read up to 25.000 profiles per second is another important advantage provided in real time railway inspection. Consequently, a computer vision-based approach in which 3D laser cameras that could allow for contactless and fast detection of the railway surface and lateral defects such as fracture, scouring and wear with high accuracy are used in the railway inspection process was proposed in the study.

Railway tracks must be periodically inspected. This study proposes a new approach for eliminating two major disadvantages experienced during rail inspection applications performed via computer vision. The first is the blurring effect on images, resulting from physical vibration during movement on the rail lines. This effect significantly reduces the high accuracy rate expected from anomaly inspection algorithms. The second disadvantage is the need to operate in real time. This study presents a new three-stage computer vision method approach that eliminates both disadvantages. First, a three-stage pipeline architecture is implemented and IMU-assisted blur detection is performed on images taken from the left and right rail lines. Next, a convolutional neural network is used for learning. In the third test stage, anomaly detection and classification training are conducted. By performing the implementation with parallel programming on graphic processing units, a highly accurate, cost-effective computer vision rail inspection, based on image processing and capable of operating in real time, is successfully carried out.

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