Improved Sobel algorithm for defect detection of rail surfaces with enhanced efficiency and accuracy

Shi, Tian; Kong, Jianyi; Wang, Xingdong; Zhao, Liang; Guo, Zheng

doi:10.1007/s11771-016-3350-3

Cited by 77 publications

(47 citation statements)

References 16 publications

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“…The difference of gray level between the defect area and the background results in an obvious edge at the boundary, which can be applied to detect surface defects of metal planar materials. Owing to the discontinuity of pixels at the edge of the image, researchers usually employ local image differentiation technology to obtain edge detection operators, and the commonly used edge detection templates of metal planar materials surface defects include Prewitt [ 43 ], Sobel [ 39 , 44 ], and Canny [ 45 ] operators, Figure 4 shows the detection results of these primitive operators on the same defect sample. These operators also have their own shortcomings, and many researchers have optimized them to achieve better results.…”

Section: Taxonomy Of Two-dimension Defect Detection Methodsmentioning

confidence: 99%

Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Fang

Luo

Zhou

et al. 2020

Sensors

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The computer-vision-based surface defect detection of metal planar materials is a research hotspot in the field of metallurgical industry. The high standard of planar surface quality in the metal manufacturing industry requires that the performance of an automated visual inspection system and its algorithms are constantly improved. This paper attempts to present a comprehensive survey on both two-dimensional and three-dimensional surface defect detection technologies based on reviewing over 160 publications for some typical metal planar material products of steel, aluminum, copper plates and strips. According to the algorithm properties as well as the image features, the existing two-dimensional methodologies are categorized into four groups: statistical, spectral, model, and machine learning-based methods. On the basis of three-dimensional data acquisition, the three-dimensional technologies are divided into stereoscopic vision, photometric stereo, laser scanner, and structured light measurement methods. These classical algorithms and emerging methods are introduced, analyzed, and compared in this review. Finally, the remaining challenges and future research trends of visual defect detection are discussed and forecasted at an abstract level.

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Section: Taxonomy Of Two-dimension Defect Detection Methodsmentioning

confidence: 99%

Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Fang

Luo

Zhou

et al. 2020

Sensors

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“…It is also easy to implement the Sobel algorithm on the FPGA development kit. Sobel edge detection algorithm is a scanning process with 3x3 size and matrices on the image matrix [21][22][23]. and matrices are given in the equation 1.…”

Section: Proposed Methodsmentioning

confidence: 99%

A Fault Diagnosis Approach for Rail Surface Anomalies Using FPGA in Railways

Yaman

Karaköse

Akın

2017

ijamec

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Railway transport is a widely used means of transportation for passenger and cargo transportation. In recent years, more emphasis has been placed on railway transport. With the development of high-speed trains, it has become important for passenger transport. Due to the heavy construction of the train, continuous failures occur in the railway line. Various methods of inspection are available to detect these failures. In case of early fault detection and repair of major accidents can be prevented. In this study, an FPGA based method is proposed for rail surface inspection and fault diagnosis. The proposed method is realized by image processing with FPGA. The image is taken on the railway line with the camera attached to the FPGA development board. Pre-processing is performed on the obtained image. Edge extraction is applied to the image after pre-processing. The rail surface is detected using the image obtained as a result of edge extraction. The proposed method works in real time to monitor and diagnose faults. It detects many defects on the track surface. In addition, the proposed method measures the size of the fault on the rail surface. In this study, FPGA based condition monitoring device was developed. An architecture has been developed for implementing the proposed method with FPGA. This work using FPGA technology is low cost and fast compared to other methods. The proposed method is quite advantageous because of its realtime operation.

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“…In order to avoid the false detection, Borselli et al [27] modified Sobel operator by applying thresholding to convert the grayscale image to binary matrix. Further, Shi et al in [28] developed eight directional templates to obtain more comprehensive edge information than the original Sobel operator which only has horizontal and vertical directions. Fig.…”

Section: ) Edge-basedmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

339

111

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Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This paper attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs, hot-and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: Statistical, spectral, model-based and machine learning. These literatures are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

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Improved Sobel algorithm for defect detection of rail surfaces with enhanced efficiency and accuracy

Cited by 77 publications

References 16 publications

Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

A Fault Diagnosis Approach for Rail Surface Anomalies Using FPGA in Railways

Automated Visual Defect Detection for Flat Steel Surface: A Survey

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