Development of Intelligent Obstacle Detection System on Railway Tracks for Yard Locomotives Using CNN

Чернов, А. В.; Butakova, Maria A.; Guda, Alexander N.; Shevchuk, Petr S.

doi:10.1007/978-3-030-58462-7_3

Cited by 10 publications

(6 citation statements)

References 21 publications

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“…It should be noted, however, that when examining related works about railway computer vision applications [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ], there is still much room left for the field to mature. Furthermore, it is stressed that existing algorithms and approaches are not universal, since railway signage differs in various countries around the world.…”

Section: Related Literature For Object Detection In the Railway Industrymentioning

confidence: 99%

Automatic Switching of Electric Locomotive Power in Railway Neutral Sections Using Image Processing

Mcineka,

Pillay,

Moorgas

et al. 2024

J. Imaging

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This article presents a computer vision-based approach to switching electric locomotive power supplies as the vehicle approaches a railway neutral section. Neutral sections are defined as a phase break in which the objective is to separate two single-phase traction supplies on an overhead railway supply line. This separation prevents flashovers due to high voltages caused by the locomotives shorting both electrical phases. The typical system of switching traction supplies automatically employs the use of electro-mechanical relays and induction magnets. In this paper, an image classification approach is proposed to replace the conventional electro-mechanical system with two unique visual markers that represent the ‘Open’ and ‘Close’ signals to initiate the transition. When the computer vision model detects either marker, the vacuum circuit breakers inside the electrical locomotive will be triggered to their respective positions depending on the identified image. A Histogram of Oriented Gradient technique was implemented for feature extraction during the training phase and a Linear Support Vector Machine algorithm was trained for the target image classification. For the task of image segmentation, the Circular Hough Transform shape detection algorithm was employed to locate the markers in the captured images and provided cartesian plane coordinates for segmenting the Object of Interest. A signal marker classification accuracy of 94% with 75 objects per second was achieved using a Linear Support Vector Machine during the experimental testing phase.

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Section: Related Literature For Object Detection In the Railway Industrymentioning

confidence: 99%

Automatic Switching of Electric Locomotive Power in Railway Neutral Sections Using Image Processing

Mcineka,

Pillay,

Moorgas

et al. 2024

J. Imaging

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“…Ristić-Durrant et al [3] provide an in-depth analysis of purely optical-based object detection systems for railways and compare their performances and limitations. Simple stereo camera setups can provide dense and accurate depth information, but due to the limited baseline only at a short range of approximately 80 m [4,[27][28][29] Ukai [30] employs an active vision approach to extend the range of vision-based approaches. In further research, this is fused with Radar sensors for depth information [31,32].…”

Section: Railway Obstacle Detectionmentioning

confidence: 99%

High-Precision Low-Cost Gimballing Platform for Long-Range Railway Obstacle Detection

Assaf

Cornelius

Cadena

et al. 2022

Sensors

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Increasing demand for rail transportation results transportation by rail, resulting in denser and more high-speed usage of the existing railway network, making makes new and more advanced vehicle safety systems necessary. Furthermore, high traveling speeds and the greatlarge weights of trains lead to long braking distances—all of which necessitates Long braking distances, due to high travelling speeds and the massive weight of trains, necessitate a Long-Range Obstacle Detection (LROD) system, capable of detecting humans and other objects more than 1000 m in advance. According to current research, only a few sensor modalities are capable of reaching this far and recording sufficiently accurate enoughdata to distinguish individual objects. The limitation of these sensors, such as a 1D-Light Detection and Ranging (LiDAR), is however a very narrow Field of View (FoV), making it necessary to use ahigh-precision means of orienting to target them at possible areas of interest. To close this research gap, this paper presents a novel approach to detecting railway obstacles by developinga high-precision pointing mechanism, for the use in a future novel railway obstacle detection system In this work such a high-precision pointing mechanism is developed, capable of targeting aiming a 1D-LiDAR at humans or objects at the required distance. This approach addresses To address the challenges of a low target pricelimited budget, restricted access to high-precision machinery and equipment as well as unique requirements of our target application., a novel pointing mechanism has been designed and developed. By combining established elements from 3D printers and Computer Numerical Control (CNC) machines with a double-hinged lever system, simple and cheaplow-cost components are capable of precisely orienting an arbitrary sensor platform. The system’s actual pointing accuracy has been evaluated using a controlled, in-door, long-range experiment. The device was able to demonstrate a precision of 6.179 mdeg, which is at the limit of the measurable precision of the designed experiment.

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“…The system proposed in [ 25 ] is also a short-range system mainly aimed at object detection in shunting mode with train speeds less than 45 km/h. Another obstacle detection system for shunting locomotives is presented in [ 64 ], this one being a stereo-vision and CNN based system for determining the safe distance between the locomotive and the wagon coupling device. However, no details are given about distance estimation of the objects detected.…”

Section: Vision-based On-board Obstacle Detection In Railwaysmentioning

confidence: 99%

A Review of Vision-Based On-Board Obstacle Detection and Distance Estimation in Railways

Ristić-Durrant

Franke

Michels

2021

Sensors

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This paper provides a review of the literature on vision-based on-board obstacle detection and distance estimation in railways. Environment perception is crucial for autonomous detection of obstacles in a vehicle’s surroundings. The use of on-board sensors for road vehicles for this purpose is well established, and advances in Artificial Intelligence and sensing technologies have motivated significant research and development in obstacle detection in the automotive field. However, research and development on obstacle detection in railways has been less extensive. To the best of our knowledge, this is the first comprehensive review of on-board obstacle detection methods for railway applications. This paper reviews currently used sensors, with particular focus on vision sensors due to their dominant use in the field. It then discusses and categorizes the methods based on vision sensors into methods based on traditional Computer Vision and methods based on Artificial Intelligence.

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Development of Intelligent Obstacle Detection System on Railway Tracks for Yard Locomotives Using CNN

Cited by 10 publications

References 21 publications

Automatic Switching of Electric Locomotive Power in Railway Neutral Sections Using Image Processing

Automatic Switching of Electric Locomotive Power in Railway Neutral Sections Using Image Processing

High-Precision Low-Cost Gimballing Platform for Long-Range Railway Obstacle Detection

A Review of Vision-Based On-Board Obstacle Detection and Distance Estimation in Railways

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