Morphological Detection and Extraction of Rail Surface Defects

Nieniewski, Mariusz

doi:10.1109/tim.2020.2975454

Cited by 56 publications

(22 citation statements)

References 21 publications

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“…The RSDD dataset has been extensively applied in the literature [ 15 , 17 , 36 , 38 , 42 , 43 ], and thereby, our defect detection approach can be properly compared with previous ones to evaluate the performance. The same evaluation criteria, namely the precision (Pre), recall (Rec), and F-measure (F), were adopted at both the pixel and defect levels.…”

Section: Resultsmentioning

confidence: 99%

“…The publicly available data resource namely the rail surface discrete defects (RSDD) dataset [ 36 ], which has been extensively applied in evaluating different approaches for railhead defect detection (e.g., [ 17 , 37 , 38 ]), constituted the data foundation of this research. It comprises two types of track surface images: the Type-I RSDD dataset contained 67 challenging images acquired from express tracks, and the Type-II RSDD dataset contained 128 challenging images acquired from ordinary/heavy haul tracks.…”

Section: Methodsmentioning

confidence: 99%

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Wavelet Scattering and Neural Networks for Railhead Defect Identification

Yang

2021

Materials

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Accurate and automatic railhead inspection is crucial for the operational safety of railway systems. Deep learning on visual images is effective in the automatic detection of railhead defects, but either intensive data requirements or ignoring defect sizes reduce its applicability. This paper developed a machine learning framework based on wavelet scattering networks (WSNs) and neural networks (NNs) for identifying railhead defects. WSNs are functionally equivalent to deep convolutional neural networks while containing no parameters, thus suitable for non-intensive datasets. NNs can restore location and size information. The publicly available rail surface discrete defects (RSDD) datasets were analyzed, including 67 Type-I railhead images acquired from express tracks and 128 Type-II images captured from ordinary/heavy haul tracks. The ultimate validation accuracy reached 99.80% and 99.44%, respectively. WSNs can extract implicit signal features, and the support vector machine classifier can improve the learning accuracy of NNs by over 6%. Three criteria, namely the precision, recall, and F-measure, were calculated for comparison with the literature. At the pixel level, the developed approach achieved three criteria of around 90%, outperforming former methods. At the defect level, the recall rates reached 100%, indicating all labeled defects were identified. The precision rates were around 75%, affected by the insignificant misidentified speckles (smaller than 20 pixels). Nonetheless, the developed learning framework was effective in identifying railhead defects.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Wavelet Scattering and Neural Networks for Railhead Defect Identification

Yang

2021

Materials

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“…CNNs have also been utilised to perform railcar safety inspection [154], determine the area of the rails ahead [155] detecting objects ahead [156], detect multiple catenary systems and support components [157]- [159], tracking joints [160] and detecting track defects [161], [162].…”

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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“…In the last decade, many scholars have conducted research on the detection methods of rail surface defects. ese methods mainly solve three problems, namely, the classification of rail surface defects [28,29], location of rail surface defects [30][31][32][33], and pixel-level segmentation of rail surface defects [34][35][36][37]. Among them, the pixel-level segmentation of rail surface defects is a key research problem.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, the pixel-level segmentation of rail surface defects is a key research problem. Nieniewski [34] proposed a detection method based on morphological processing for pixel-level extraction of rail surface defects. e main advantage of this method is the fast detection speed that can reach 50 ms/ frame.…”

Section: Introductionmentioning

confidence: 99%

A Defect Detection Method for Rail Surface and Fasteners Based on Deep Convolutional Neural Network

Zheng

et al. 2021

Computational Intelligence and Neuroscience

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As a result of long-term pressure from train operations and direct exposure to the natural environment, rails, fasteners, and other components of railway track lines inevitably produce defects, which have a direct impact on the safety of train operations. In this study, a multiobject detection method based on deep convolutional neural network that can achieve nondestructive detection of rail surface and fastener defects is proposed. First, rails and fasteners on the railway track image are localized by the improved YOLOv5 framework. Then, the defect detection model based on Mask R-CNN is utilized to detect the surface defects of the rail and segment the defect area. Finally, the model based on ResNet framework is used to classify the state of the fasteners. To verify the robustness and effectiveness of our proposed method, we conduct experimental tests using the ballast and ballastless railway track images collected from Shijiazhuang-Taiyuan high-speed railway line. Through a variety of evaluation indexes to compare with other methods using deep learning algorithms, experimental results show that our method outperforms others in all stages and enables effective detection of rail surface and fasteners.

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Morphological Detection and Extraction of Rail Surface Defects

Cited by 56 publications

References 21 publications

Wavelet Scattering and Neural Networks for Railhead Defect Identification

Wavelet Scattering and Neural Networks for Railhead Defect Identification

A Deep Learning Approach Towards Railway Safety Risk Assessment

A Defect Detection Method for Rail Surface and Fasteners Based on Deep Convolutional Neural Network

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