Automated visual inspection of target parts for train safety based on deep learning

Zhou, Fuqiang; Song, Yang; Liu, Liu; Zheng, Dongtian

doi:10.1049/iet-its.2016.0338

Cited by 19 publications

(5 citation statements)

References 19 publications

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“…Additionally, the paper notes that previous computer vision-based methods for detecting abnormal targets by contrasting them with standard images are heuristic and can result in many false alarms. Zhou et al propose a method that combines traditional visual inspection with deep learning by implementing a stacked auto-encoder convolutional neural network (SAE-CNN) to improve inspection accuracy for automated visual inspection of target parts for train safety [20]. The researchers achieved an accuracy rate of over 98% in their final experiment using the proposed visual inspection method combining traditional feature extraction with deep learning.…”

Section: Bolt Detection Using Deep Learningmentioning

confidence: 99%

BoltVision: A Comparative Analysis of CNN, CCT, and ViT in Achieving High Accuracy for Missing Bolt Classification in Train Components

Alif,

Hussain,

Tucker

et al. 2024

Machines

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Maintenance and safety inspection of trains is a critical element of providing a safe and reliable train service. Checking for the presence of bolts is an essential part of train inspection, which is currently, typically carried out during visual inspections. There is an opportunity to automate bolt inspection using machine vision with edge devices. One particular challenge is the implementation of such inspection mechanisms on edge devices, which necessitates using lighter models to ensure efficiency. Traditional methods have often fallen short of the required object detection performance, thus demonstrating the need for a more advanced approach. To address this challenge, researchers have been exploring the use of deep learning algorithms and computer vision techniques to improve the accuracy and reliability of bolt detection on edge devices. High precision in identifying absent bolts in train components is essential to avoid potential mishaps and system malfunctions. This paper presents “BoltVision”, a comparative analysis of three cutting-edge machine learning models: convolutional neural networks (CNNs), vision transformers (ViTs), and compact convolutional transformers (CCTs). This study illustrates the superior assessment capabilities of these models and discusses their effectiveness in addressing the prevalent issue of edge devices. Results show that BoltVision, utilising a pre-trained ViT base, achieves a remarkable 93% accuracy in classifying missing bolts. These results underscore the potential of BoltVision in tackling specific safety inspection challenges for trains and highlight its effectiveness when deployed on edge devices characterised by constrained computational resources. This attests to the pivotal role of transformer-based architectures in revolutionising predictive maintenance and safety assurance within the rail transportation industry.

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Section: Bolt Detection Using Deep Learningmentioning

confidence: 99%

BoltVision: A Comparative Analysis of CNN, CCT, and ViT in Achieving High Accuracy for Missing Bolt Classification in Train Components

Alif,

Hussain,

Tucker

et al. 2024

Machines

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“…It can describe the appearance or shape of an object by using the gradient or density distribution in the edge direction of the image, which can reduce the impact of light and shadow [24]. Since the image of angle cocks is taken from a different environment, the image quality will be affected by many factors such as illumination and clarity [25]. The HOG feature contains the process of compressing the illumination and the edge [26], it can solve the problem of large change in the illumination and background contrast.…”

Section: Hog Featurementioning

confidence: 99%

Coarse-to-Fine Localization for Detecting Misalignment State of Angle Cocks

Lei,

Cao,

2023

Sensors

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The state of angle cocks determines the air connectivity of freight trains, and detecting their state is helpful to improve the safety of the running trains. Although the current research for fault detection of angle cocks has achieved high accuracy, it only focuses on the detection of the closed state and non-closed state and treats them as normal and abnormal states, respectively. Since the non-closed state includes the fully open state and the misalignment state, while the latter may lead to brake abnormally, it is very necessary to further detect the misalignment state from the non-closed state. In this paper, we propose a coarse-to-fine localization method to achieve this goal. Firstly, the localization result of an angle cock is obtained by using the YOLOv4 model. Following that, the SVM model combined with the HOG feature of the localization result of an angle cock is used to further obtain its handle localization result. After that, the HOG feature of the sub-image only containing the handle localization result continues to be used in the SVM model to detect whether the angle cock is in the non-closed state or not. When the angle cock is in the non-closed state, its handle curve is fitted by binarization and window search, and the tilt angle of the handle is calculated by the minimum bounding rectangle. Finally, the misalignment state is detected when the tilt angle of the handle is less than the threshold. The effectiveness and robustness of the proposed method are verified by extensive experiments, and the accuracy of misalignment state detection for angle cocks reaches 96.49%.

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“…As shown in Figure 1 , bolts play an important role in connecting and fixing various components on the train and are widely used. Traditional detection methods are generally manual troubleshooting of bolt faults by professional train inspectors, but these methods are inefficient, with high costs, human error, and an inability to realize dynamic detection [ 5 ]. Therefore, methods using computer vision have been developed.…”

Section: Introductionmentioning

confidence: 99%

Scattered Train Bolt Point Cloud Segmentation Based on Hierarchical Multi-Scale Feature Learning

Zeng

Gao

et al. 2023

Sensors

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In view of the difficulty of using raw 3D point clouds for component detection in the railway field, this paper designs a point cloud segmentation model based on deep learning together with a point cloud preprocessing mechanism. First, a special preprocessing algorithm is designed to resolve the problems of noise points, acquisition errors, and large data volume in the actual point cloud model of the bolt. The algorithm uses the point cloud adaptive weighted guided filtering for noise smoothing according to the noise characteristics. Then retaining the key points of the point cloud, this algorithm uses the octree to partition the point cloud and carries out iterative farthest point sampling in each partition for obtaining the standard point cloud model. The standard point cloud model is then subjected to hierarchical multi-scale feature extraction to obtain global features, which are combined with local features through a self-attention mechanism, while linear interpolation is used to further expand the perceptual field of local features of the model as a basis for segmentation, and finally the segmentation is completed. Experiments show that the proposed algorithm could deal with the scattered bolt point cloud well, realize the segmentation of train bolt and background, and could achieve high segmentation accuracy, which has important practical significance for train safety detection.

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Automated visual inspection of target parts for train safety based on deep learning

Cited by 19 publications

References 19 publications

BoltVision: A Comparative Analysis of CNN, CCT, and ViT in Achieving High Accuracy for Missing Bolt Classification in Train Components

BoltVision: A Comparative Analysis of CNN, CCT, and ViT in Achieving High Accuracy for Missing Bolt Classification in Train Components

Coarse-to-Fine Localization for Detecting Misalignment State of Angle Cocks

Scattered Train Bolt Point Cloud Segmentation Based on Hierarchical Multi-Scale Feature Learning

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