Detection for Rail Surface Defects via Partitioned Edge Feature

Ni, Xuefeng; Liu, Hongli; Ma, Ziji; Wang, Chao; Liu, Jianwei

doi:10.1109/tits.2021.3058635

Cited by 49 publications

(20 citation statements)

References 38 publications

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“…In the process of acquiring sports image poses, four sets of Gabor filters with different directions of light are used to extract the direction information in the pictures. e filter function is set in the filter as a Gaussian function modulated by a sine function, which has a certain direction selectivity and spatial connectivity [10]. e filter function can be embodied by the following formula:…”

Section: Sports Image Acquisitionmentioning

confidence: 99%

A Motion Image Pose Contour Extraction Method Based on B-Spline Wavelet

Chen

2021

International Journal of Antennas and Propagation

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In order to improve the accuracy of traditional motion image pose contour extraction and shorten the extraction time, a motion image pose contour extraction method based on B-spline wavelet is proposed. Moving images are acquired through the visual system, the information fusion process is used to perform statistical analysis on the images containing motion information, the location of the motion area is determined, convolutional neural network technology is used to preprocess the initial motion image pose contour, and B-spline wavelet theory is used. The preprocessed motion image pose contour is detected, combined with the heuristic search method to obtain the pose contour points, and the motion image pose contour extraction is completed. The simulation results show that the proposed method has higher accuracy and shorter extraction time in extracting motion image pose contours.

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Section: Sports Image Acquisitionmentioning

confidence: 99%

A Motion Image Pose Contour Extraction Method Based on B-Spline Wavelet

Chen

2021

International Journal of Antennas and Propagation

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“…They showed that the softmax classifier gave better results in the proposed method. Ni et al [43] experimentally performed rail region extraction, edge detection, detect contour filling in their proposed study to detect rail surface defects. Bojarczak and Lesiak [44] used a deep learning network implemented in the Tensorflow environment, such as FCN-8, to experimentally prevent the brightness of the images from affecting the segmentation success in their proposed study to detect rail surface defects.…”

Section: Referencesmentioning

confidence: 99%

A New Rail Surface Defects Detection Approach Using 3D Laser Cameras Based on ResNet50

Santur¹,

Yilmazer²,

Karaköse³

et al. 2022

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Rail transportation systems, which are used as one of the most common means of transportation worldwide, should be regularly inspected to prevent accidents that may occur. The rail condition monitoring can be performed in high accuracy and real time using computer vision, deep learning algorithms today. In this study, a new deep learning based approach using 3D laser cameras for rail inspection is presented. In the proposed approach, two 3D laser cameras placed on a real train, seeing the rail line from the left and right surfaces were used. These data consisting of sensitive distance value constitute the input data of the ResNet50 transfer learning model. The training was carried out on Nvidia Cuda supported graphics processing units using ResNet50 Convolutional Neural Network. During the test phase, the operation speed and accuracy rate of the method was measured by repeating the process on real-time rail profiles. The accuracy rate was calculated as 94%. As a result a new approach is presented based on deep learning using 3D laser cameras for rail inspection is presented.

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“…The method in Niu et al ’s (2021) work is proposed based on the bi-level super pixel-based framework and bag-of-words feature extractor. Ni et al (2021) discussed a novel defect detection algorithm based on a partitioned edge feature. In Zhang et al ’s (2018) work, a curvature filter was embedded to retain relevant details and eliminate noise.…”

Section: Introductionmentioning

confidence: 99%

Detection algorithm of rail surface defects based on multifeature saliency fusion method

Zhai

2022

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Purpose Effective rail surface defects detection method is the basic guarantee to manufacture high-quality rail. However, the existed visual inspection methods have disadvantages such as poor ability to locate the rail surface region and high sensitivity to uneven reflection. This study aims to propose a bionic rail surface defect detection method to obtain the high detection accuracy of rail surface defects under uneven reflection environments. Design/methodology/approach Through this bionic rail surface defect detection algorithm, the positioning and correction of the rail surface region can be computed from maximum run-length smearing (MRLS) and background difference. A saliency image can be generated to simulate the human visual system through some features including local grayscale, local contrast and edge corner effect. Finally, the meanshift algorithm and adaptive threshold are developed to cluster and segment the saliency image. Findings On the constructed rail defect data set, the bionic rail surface defect detection algorithm shows good recognition ability on the surface defects of the rail. Pixel- and defect-level index in the experimental results demonstrate that the detection algorithm is better than three advanced rail defect detection algorithms and five saliency models. Originality/value The bionic rail surface defect detection algorithm in the production process is proposed. Particularly, a method based on MRLS is introduced to extract the rail surface region and a multifeature saliency fusion model is presented to identify rail surface defects.

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Detection for Rail Surface Defects via Partitioned Edge Feature

Cited by 49 publications

References 38 publications

A Motion Image Pose Contour Extraction Method Based on B-Spline Wavelet

A Motion Image Pose Contour Extraction Method Based on B-Spline Wavelet

A New Rail Surface Defects Detection Approach Using 3D Laser Cameras Based on ResNet50

Detection algorithm of rail surface defects based on multifeature saliency fusion method

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