A new computer vision approach for active pantograph control

International Journal of Applied Mathematics, Electronics and Computers

Yaman

Akın

2016

Today, railway transportation is one of the transport modes commonly used. Compared to other transport modes, railway traffic is highly critical. Multiple railway vehicles run constantly on one or two lines. Rail switch passages are used to prevent locomotives from colliding with one another and avoid traffic disruptions. Through switch passages, locomotives pass from one line to another. Friction between rail and wheels on switch passages is considerably high. This friction leads to failures on switch passages. Unless these failures are diagnosed early and remedied, significant accidents emerge.In this study, a new approach based on image processing has been presented for detection of rail switch passages on railway lines. A test vehicle has been created in order to test the proposed approach and apply it on a real-time system. Railway line is monitored by digital cameras fixed on this test vehicle. Image-processing approach is developed on the real-time images captured from the railway line and the switch passages on the line are detected. In addition, by specifying the train route, the fault which occurring at the point of the switches is detected. The image-processing approach consists of three main parts including pre-processing, feature extraction and processing of the features obtained. At the pre-processing stage, the basic image processing methods are used. At the feature extraction stage, Canny edge extraction algorithm is used and hence the edges in the image are detected. Hough transform method is used at the stage of processing of the extracted features. Following Hough transform stage, straight lines and angles of these lines are obtained on the image. Taking into account the angle of each straight line, the junction points of the lines are calculated. Thus, rail switch passage and switch types are detected. The proposed image-processing approach is highly fast and real time-based. Compared to the existing studies in the literature, it is seen that the proposed method gives fast and successful results. This study intends to diagnose the failures on switch passages early and prevent potential accidents.

Section: The Methods Proposed For Switchmentioning

confidence: 99%

“…The pseudo code of Hough transform used in the proposed method is given in Figure 8. [24] A binary-based image obtained as a result of edge extraction is primarily used in the pseudo code of Hough transform given in Figure 7. The straight lines are expressed as in equation 1.…”

Section: The Methods Proposed For Switchmentioning

confidence: 99%

Real time implementation for fault diagnosis and condition monitoring approach using image processing in railway switches

International Journal of Applied Mathematics, Electronics and Computers

Yaman

Akın

2016

“…Edge detection methods are very efficient for feature extraction in the other detection problems [5][6][7], but performing feature extraction from the road surface is not only edge detection or texture analysis problem [8]. Colour changes and noise in received image make difficult to defect detection.…”

Section: Introductionmentioning

confidence: 99%

A Fast and Adaptive Road Defect Detection Approach Using Computer Vision with Real Time Implementation

Akarsu

International Journal of Applied Mathematics, Electronics and Computers

Parlak

et al. 2016

Self Cite

Road defect is one of the most important factors for traffic accident. Therefore, these defects should be corrected as soon as possible. It usually occurs cracks, rutting, and potholes in road surface. These errors are based on the fact that people have recognized and fixed these errors in our day. But if these errors are not corrected in a short time, the size of the error grows day by day. There are various methods used to detect road errors in the literature. One of these methods is the use of computer vision. There are various types of roads in real life. Since the studies in the literature have been carried out only by taking into account one type of road, the accuracy rates decrease when these studies are used in different types of roads. In the study carried out, different roads have been made adaptive by the operations performed in the detection of road errors from the received images. Images taken from the camera on a vehicle are used for the study. The study applied is ensured to have high accuracy rates in different types of roads via customization. In the second stage, the image blurred by using median filter and the unprocessed images are collected, and the darkest parts of the image are brought into the forefront. The image is converted into a binary image and improved by mathematical morphological operations. As a result of the operations performed, which of the five classes including un-cracked roads, superficial crack, crocodile crack, linear crack and transverse crack the roads belong to is determined. In the study carried out, the fact that it is fast and that its accuracy rates are good indicate that it can be used in real life.

“…When three-dimensional laser cameras are compared with normal cameras, they involve both rgb data and precise distance information on the two-dimensional plane. Because of these features, they are widely used with the aim of finding faults in industrial products including rail inspection [9][10][11][12]. Although the cost rail inspection with 3-d cameras is higher, it is the most advantageous method in terms of accuracy rate and operating speed.…”

Section: Introductionmentioning

confidence: 99%

Learning Based Experimental Approach For Condition Monitoring Using Laser Cameras In Railway Tracks

Santur¹,

International Journal of Applied Mathematics, Electronics and Computers

Akın

2016

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.