A New Computer Vision Based Method for Rail Track Detection and Fault Diagnosis in Railways

Karaköse, Mehmet; Yaman, Orhan; Bayğın, Mehmet; Murat, Kagan; Akın, Erhan

doi:10.18178/ijmerr.6.1.22-27

Cited by 69 publications

(34 citation statements)

References 13 publications

(12 reference statements)

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“…Karakose et al (14) proposed a computer-vision-based monitoring method for the detection of faulty tracks. Such a method is being increasingly used in railway systems.…”

Section: Automatic Track Inspection Technologiesmentioning

confidence: 99%

Offline Deep-learning-based Defective Track Fastener Detection and Inspection System

Hsieh¹,

Lin²,

Tsai³

et al. 2020

Sensors and Materials

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Railway track fasteners are used to fasten the railway track onto the crosstie and improve the train's stability and safety. Automatic detection systems have been developed for track safety. Most of these systems deployed line scan sensors to capture high-quality track images. These sensors can capture high-resolution images, but they are also expensive. In addition, the recognition kernels range from traditional computer vision to deep learning methods. In this study, we set up a track fastener sensing device on a flat track car by using general sport cameras and LED lamps to capture images of track fasteners. You Only Look Once (Yolo) v3 is also used instead of earlier convolution neural networks (CNNs) for defect inspection. A cloud server is built for users to queue their captured fastener videos to the first buffer for upload, and uploaded videos can be queued to a second buffer for defective track fastener detection. The trained Yolo v3 neural network classification module is encapsulated as a web application interface (API) for performing the task. In experiments, track fastener videos along a total of 70 km of track were captured with a resolution of 1920 × 1080 at a speed of up to 35 km/h. Six normal and four defective fastener types were defined for inspection. We split the dataset into 80% for training and 20% for testing. The average precision rates for normal and defective fasteners were 83 and 89%, respectively. Finally, the coordinates of defective fasteners were interpolated from GPS positions recorded by a sport camera. The nearest hectometer stake and the offset of each defective fastener were calculated to assist track workers to find the defective fasteners and fix them.

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“…Karakose et al (14) proposed a computer-vision-based monitoring method for the detection of faulty tracks. Such a method is being increasingly used in railway systems.…”

Section: Automatic Track Inspection Technologiesmentioning

confidence: 99%

Offline Deep-learning-based Defective Track Fastener Detection and Inspection System

Hsieh¹,

Lin²,

Tsai³

et al. 2020

Sensors and Materials

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“…Thus, a faster image processing algorithm is required. Various image processing algorithms were tested and it was noted that the most reliable amongst them is the Canny edge detector [17] [18], as it enables the edges of the railway track to be analyzed with almost zero delays [19]. Therefore, in this research a very effective two-stage process has been developed for overcoming this deficiency.…”

Section: Introductionmentioning

confidence: 99%

Real Time Identification of Railway Track Surface Faults using Canny Edge Detector and 2D Discrete Wavelet Transform

Shah¹,

Chowdhry²,

Memon³

et al. 2020

AETiC

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Usually, railway accidents are caused by train derailment, the mechanical failure of tracks, such as broken rails often caused by lack of railway condition monitoring. Such monitoring could identify track surface faults, such as squats, that act as a catalyst for the track to crack and ultimately break. The research presented in this paper enables real-time identification of railway track faults using image processing techniques such as Canny edge detection and 2D discrete wavelet transformation. The Canny edge detection outperforms traditional track damage detection techniques including Axle Based Acceleration using Inertial Measurement Units and is as reliable as Fiber Bragg Grating. The Canny edge detection employed can identify squats in real-time owing to its specific threshold amplitude using a camera module mounted on a specially designed handheld Track Recording Vehicle (TRV). The 2D discrete wavelet transformation validates the insinuation of the Canny edge detector regarding track damage and furthermore determines damage severity, by applying high sub band frequency filter. The entire algorithm works on a Raspberry Pi 3 B+ utilizing an OpenCV API. When tested using an actual rail track, the algorithm proved reliable at determining track surface damage in real-time. Although wavelet transformation performs better than Canny edge detection in terms of determining the severity of track surface damage, it has processing overheads that become a bottleneck in real-time. To overcome this deficiency a very effective two-stage process has been developed.

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“…Karakose [6] proposed a computer-based visual rail condition monitoring in which camera is placed on top of the train and neighbor rail images are taken. On these images, the edge and feature extraction methods are applied to determine the rails.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Machine Vision Based Railway Infrastructure Inspection and Monitoring Using Uav

et al. 2019

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Traditionally, railway inspection and monitoring are considered a crucial aspect of the system and are done by human inspectors. Rapid progress of the machine vision-based systems enables automated and autonomous rail track detection and railway infrastructure monitoring and inspection with flexibility and ease of use. In recent years, several prototypes of vision based inspection system have been proposed, where most have various vision sensors mounted on locomotives or wagons. This paper explores the usage of the UAVs (drones) in railways and computer vision based monitoring of railway infrastructure. Employing drones for such monitoring systems enables more robust and reliable visual inspection while providing a cost effective and accurate means for monitoring of the tracks. By means of a camera placed on a drone the images of the rail tracks and the railway infrastructure are taken. On these images, the edge and feature extraction methods are applied to determine the rails. The preliminary obtained results are promising.

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A New Computer Vision Based Method for Rail Track Detection and Fault Diagnosis in Railways

Cited by 69 publications

References 13 publications

Offline Deep-learning-based Defective Track Fastener Detection and Inspection System

Offline Deep-learning-based Defective Track Fastener Detection and Inspection System

Real Time Identification of Railway Track Surface Faults using Canny Edge Detector and 2D Discrete Wavelet Transform

Intelligent Machine Vision Based Railway Infrastructure Inspection and Monitoring Using Uav

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