A Coarse-to-Fine Model for Rail Surface Defect Detection

Yu, Hongliang; Li, Qingyong; Tan, Yunqiang; Gan, Jinrui; Wang, Jianzhu; Geng, Yangli-ao; Jia, Lei

doi:10.1109/tim.2018.2853958

Cited by 126 publications

(50 citation statements)

References 39 publications

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“…The visual saliency detection model is a process in which computer vision algorithms are used to predict which information in an image or video receives more visual attention. Yu et al [ 93 ] considered the significance of track defects and the regularity of background when detecting rail surface defects; then, they selected pure phase Fourier transform (POFTs) to locate defects. Similarly, Song et al [ 94 ] calculated saliency mapping of grayscale images in order to detect micro-cracks on the surface of steel beams, and they defined central hole-out as a square template for the convolution of filter operator and binarized saliency mapping.…”

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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“…5) The surface detection system needs to work continuously. Manual inspection is inefficient and has a low sensitivity [2], [32], which will slow down the entire manufacturing process. Compared with the manual inspection, nondestructive testing methods, such as ultrasonic testing, acoustic emission testing, and magnetic flux leakage [3], have a higher sensitivity and data interaction in the industrial inspection.…”

Section: )mentioning

confidence: 99%

“…Compared with the manual inspection, nondestructive testing methods, such as ultrasonic testing, acoustic emission testing, and magnetic flux leakage [3], have a higher sensitivity and data interaction in the industrial inspection. However, most of them are time intensive [2] to the target motion and the detected defects lack the intuitive descriptions of defects' shapes. A machine vision has more advantages in surface defects testing.…”

Section: )mentioning

confidence: 99%

“…Saliency detection is inspired by the human visual system (HVS), to detect the region of interest with the visual uniqueness in the face of complex scenes [2], [7]- [9]. By selectively acquiring the relevant regional information, HVS can greatly avoid computational waste and reduce the difficulty of the image analysis.…”

Section: Related Workmentioning

confidence: 99%

“…S URFACE inspection plays a pivotal role in improving the rail manufacturing process. Different from the online rail surface inspection system [1], [2], the manufacturing process inspection of the rail products faces the following problems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unsupervised Saliency Detection of Rail Surface Defects using Stereoscopic Images

Niu

Song

Huang

et al. 2020

IEEE Trans. Ind. Inf.

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Visual information is increasingly recognized as a useful method to detect rail surface defects due to its high efficiency and stability. However, it cannot sufficiently detect a complete defect in the complex background information. The addition of surface profiles can effectively improve this by including a 3-D information of defects. However, in high-speed detection, the traditional 3-D profile acquisition is difficult and separate from the image acquisition, which cannot satisfy the abovementioned requirements effectively. Therefore, an unsupervised stereoscopic saliency detection method based on a binocular line-scanning system is proposed in this article. This method can simultaneously obtain a highly precise image as well as profile information while also avoids the decoding distortion of the structured light reconstruction method. In our method, a global low-rank nonnegative reconstruction algorithm with a background constraint is proposed. Unlike the low-rank recovery model, the algorithm has a more comprehensive low rank and background clustering properties. Furthermore, outlier detection based on the geometric properties of the rail surface is also proposed in this method. Finally, the image saliency results and depth outlier detection results are associated with the collaborative fusion, and a dataset (RSDDS-113) containing the rail surface defects is established for the experimental verification. The experimental results demonstrate that our method can obtain a mean absolute error of 0.09 and area under the ROC curve of 0.94, better than 15 state-of-the-art algorithms.

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Research on Detection Method of Internal Defects of Metal Materials Based on Computer Vision

Zhang

Zhao

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A Coarse-to-Fine Model for Rail Surface Defect Detection

Cited by 126 publications

References 39 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

Unsupervised Saliency Detection of Rail Surface Defects using Stereoscopic Images

Research on Detection Method of Internal Defects of Metal Materials Based on Computer Vision

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