A review of applications of visual inspection technology based on image processing in the railway industry

Liu, Scarlett; Wang, Quandong; Luo, Yuan

doi:10.1093/tse/tdz007

Cited by 84 publications

(33 citation statements)

References 45 publications

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“…A railway system contains a wealth of data, and visual processing technology can play an essential role in the industry's future. The most up-to-date applications were reviewed in [179].…”

Section: Related Work In Railway Systemsmentioning

confidence: 99%

A Deep Learning Approach Towards Railway Safety Risk Assessment

Alawad

Kaewunruen

2020

IEEE Access

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Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks.

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“…A railway system contains a wealth of data, and visual processing technology can play an essential role in the industry's future. The most up-to-date applications were reviewed in [179].…”

Section: Related Work In Railway Systemsmentioning

confidence: 99%

A Deep Learning Approach Towards Railway Safety Risk Assessment

Alawad

Kaewunruen

2020

IEEE Access

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“…With the development of computer vision techniques, it has become possible for railway systems to use visual inspection technology to replace manual inspection. Liu et al [3] provided a full review of visual inspection applications based on image processing in the railway industry for rail surface, track component wear, rail component identification, train body components, etc.…”

Section: Introductionmentioning

confidence: 99%

Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning

et al. 2021

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Railway wheelsets are the key to ensuring the safe operation of trains. To achieve zero-defect production, railway equipment manufacturers must strictly control every link in the wheelset production process. The press-fit curve output by the wheelset assembly machine is an essential indicator of the wheelset’s assembly quality. The operators will still need to manually and individually recheck press-fit curves in our practical case. However, there are many uncertainties in the manual inspection. For example, subjective judgment can easily cause inconsistent judgment results between different inspectors, or the probability of human misinterpretation can increase as the working hours increase. Therefore, this study proposes an intelligent railway wheelset inspection system based on deep learning, which improves the reliability and efficiency of manual inspection of wheelset assembly quality. To solve the severe imbalance in the number of collected images, this study establishes a predicted model of press-fit quality based on a deep Siamese network. Our experimental results show that the precision measurement is outstanding for the testing dataset contained 3863 qualified images and 28 unqualified images of press-fit curves. The proposed system will serve as a successful case of a paradigm shift from traditional manufacturing to digital manufacturing.

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“…Besides, the image signal is prone to observer bias and leads to unreliability [7,8]. On the other hand, video recording generated a vast amount of data and required a high computing performance [9]. These drawbacks limited the use of two-dimensional visual analysis in gait analysis.…”

Section: Introductionmentioning

confidence: 99%

Low‐Cost Multisensor Integrated System for Online Walking Gait Detection

Yan

Wei

et al. 2021

Journal of Sensors

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A three-dimensional motion capture system is a useful tool for analysing gait patterns during walking or exercising, and it is frequently applied in biomechanical studies. However, most of them are expensive. This study designs a low-cost gait detection system with high accuracy and reliability that is an alternative method/equipment in the gait detection field to the most widely used commercial system, the virtual user concept (Vicon) system. The proposed system integrates mass-produced low-cost sensors/chips in a compact size to collect kinematic data. Furthermore, an x86 mini personal computer (PC) running at 100 Hz classifies motion data in real-time. To guarantee gait detection accuracy, the embedded gait detection algorithm adopts a multilayer perceptron (MLP) model and a rule-based calibration filter to classify kinematic data into five distinct gait events: heel-strike, foot-flat, heel-off, toe-off, and initial-swing. To evaluate performance, volunteers are requested to walk on the treadmill at a regular walking speed of 4.2 km/h while kinematic data are recorded by a low-cost system and a Vicon system simultaneously. The gait detection accuracy and relative time error are estimated by comparing the classified gait events in the study with the Vicon system as a reference. The results show that the proposed system obtains a high accuracy of 99.66% with a smaller time error (32 ms), demonstrating that it performs similarly to the Vicon system in the gait detection field.

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A review of applications of visual inspection technology based on image processing in the railway industry

Cited by 84 publications

References 45 publications

A Deep Learning Approach Towards Railway Safety Risk Assessment

A Deep Learning Approach Towards Railway Safety Risk Assessment

Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning

Low‐Cost Multisensor Integrated System for Online Walking Gait Detection

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