Research on Image-Based Detection and Recognition Technologies for Cracks on Rail Surface

Fu, Shengwen; Jiang, Zhanjun

doi:10.1109/icris.2019.00033

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…Image denoising and enhancement play an important role in rail surface crack detection and recognition [4] . Fractional calculus can change the order of fractional order adaptively when it is used to denoise and enhance the image [5] .…”

Section: Fractional Order Denoising and Enhancement Of Rail Imagesmentioning

confidence: 99%

Image segmentation of rail surface defects based on fractional order particle swarm optimization 2D-Otsu algorithm

Geng,

Sheng,

Sun

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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Under the influence of high density operation and natural environment, the rail surface will appear abrasion damage, which will affect the safety and comfort of the train. Rail surface defect detection is an important part to ensure the safe and efficient operation of railway system. In order to distinguish whether there are defects on the rail surface, a method of rail surface defect image segmentation based on FPSO 2D-Otsu algorithm is proposed. The rail image is denoised and enhanced by adaptive fractional calculus, and then the rail image is segmented by FPSO 2D-Otsu algorithm. In order to verify the accuracy of the algorithm, the proposed algorithm is compared with PSO 2D-Otsu image segmentation algorithm. The experimental results show that the accuracy of FPSO 2D-Otsu algorithm in rail image segmentation is improved from 48.76% to 83.59% compared with PSO 2D-Otsu algorithm.

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Section: Fractional Order Denoising and Enhancement Of Rail Imagesmentioning

confidence: 99%

Image segmentation of rail surface defects based on fractional order particle swarm optimization 2D-Otsu algorithm

Geng,

Sheng,

Sun

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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“…In addition, a dynamic template was designed to detect continuous crack boundaries based on the morphology of cracks. Fu and Jiang [ 4 ] proposed a visual detection and recognition technology for the detection of the surface crack of a steel rail. They firstly used a weighted median filtering algorithm to filter the track detection image, and then used a histogram equalization algorithm to enhance the filtered image.…”

Section: Literature Reviewmentioning

confidence: 99%

SurfNetv2: An Improved Real-Time SurfNet and Its Applications to Defect Recognition of Calcium Silicate Boards

Tsai

Chen

2020

Sensors

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This paper presents an improved Convolutional Neural Network (CNN) architecture to recognize surface defects of the Calcium Silicate Board (CSB) using visual image information based on a deep learning approach. The proposed CNN architecture is inspired by the existing SurfNet architecture and is named SurfNetv2, which comprises a feature extraction module and a surface defect recognition module. The output of the system is the recognized defect category on the surface of the CSB. In the collection of the training dataset, we manually captured the defect images presented on the surface of the CSB samples. Then, we divided these defect images into four categories, which are crash, dirty, uneven, and normal. In the training stage, the proposed SurfNetv2 is trained through an end-to-end supervised learning method, so that the CNN model learns how to recognize surface defects of the CSB only through the RGB image information. Experimental results show that the proposed SurfNetv2 outperforms five state-of-the-art methods and achieves a high recognition accuracy of 99.90% and 99.75% in our private CSB dataset and the public Northeastern University (NEU) dataset, respectively. Moreover, the proposed SurfNetv2 model achieves a real-time computing speed of about 199.38 fps when processing images with a resolution of 128 × 128 pixels. Therefore, the proposed CNN model has great potential for real-time automatic surface defect recognition applications.

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“…Other non-destructive testing (NDT) techniques adopted to evaluate the rail defects include the vision-based techniques, ultrasound measurements, eddy current testing (ECT) systems, accelerometers, etc. [12] The vision-based technique is one common method in current rail-defect detection studies [13,14]. The existing methods can be divided into two groups, namely, traditional image processing and image processing using machine learning.…”

Section: Introductionmentioning

confidence: 99%

Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning

et al. 2023

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Switches and crossings (S&Cs) are also known as turnouts or railway points. They are important assets in railway infrastructures and a defect in such a critical asset might lead to a long delay for the railway network and decrease the quality of service. A squat is a common rail head defect for S&Cs and needs to be detected and monitored as early as possible to avoid costly emergent maintenance activities and enhance both the reliability and availability of the railway system. Squats on the switchblade could even potentially cause the blade to break and cause a derailment. This study presented a method to collect and process vibration data at the point machine with accelerometers on three axes to extract useful features. The two most important features, the number of peaks and the total power, were found. Three different unsupervised machine learning algorithms were applied to cluster the data. The results showed that the presented method could provide promising features. The k-means and the agglomerative hierarchical clustering methods are suitable for this data set. The density-based spatial clustering of applications with noise (DBSCAN) encounters some challenges.

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Research on Image-Based Detection and Recognition Technologies for Cracks on Rail Surface

Cited by 10 publications

References 3 publications

Image segmentation of rail surface defects based on fractional order particle swarm optimization 2D-Otsu algorithm

Image segmentation of rail surface defects based on fractional order particle swarm optimization 2D-Otsu algorithm

SurfNetv2: An Improved Real-Time SurfNet and Its Applications to Defect Recognition of Calcium Silicate Boards

Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning

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