Vision-based automatic detection of steel surface defects in the cold rolling process: considering the influence of industrial liquids and surface textures

Zhao, Yong Jie; Yan, Yun; Song, Kechen

doi:10.1007/s00170-016-9489-0

Cited by 51 publications

(29 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, random industrial liquids and other interference factors increase the difficulty of detection. Zhao et al [ 122 ] developed a two-stage marking technology based on superpixels, which firstly aggregated the pixels into superpixels and then aggregated the superpixels into subregions. The boundary of the superpixel is iteratively updated until pixels with similar visual perception properties are aggregated into a superpixel.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…In order to verify and evaluate the effectiveness and robustness of the proposed method, we have adopted the NEU surface defect database established by Kechen Song [12] in our experiments. The size of each surface defect image is 200 × 200 and the number of image is 300 per class.…”

Section: Methodsmentioning

confidence: 99%

“…Li et al [11] devised a low-rank representation-based saliency detection model for textile fabric defect detection. Zhao et al [12] also presented a novel saliency detection model, which obviously improve the accuracy of automated defect segmentation.…”

Section: Introductionmentioning

confidence: 99%

Double Low-Rank and Sparse Decomposition for Surface Defect Segmentation of Steel Sheet

Zhou

Liu

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

Surface defect segmentation supports real-time surface defect detection system of steel sheet by reducing redundant information and highlighting the critical defect regions for high-level image understanding. Existing defect segmentation methods usually lack adaptiveness to different shape, size and scale of the defect object. Based on the observation that the defective area can be regarded as the salient part of image, a saliency detection model using double low-rank and sparse decomposition (DLRSD) is proposed for surface defect segmentation. The proposed method adopts a low-rank assumption which characterizes the defective sub-regions and defect-free background sub-regions respectively. In addition, DLRSD model uses sparse constrains for background sub-regions so as to improve the robustness to noise and uneven illumination simultaneously. Then the Laplacian regularization among spatially adjacent sub-regions is incorporated into the DLRSD model in order to uniformly highlight the defect object. Our proposed DLRSD-based segmentation method consists of three steps: firstly, using DLRSD model to obtain the defect foreground image; then, enhancing the foreground image to establish the good foundation for segmentation; finally, the Otsu’s method is used to choose an optimal threshold automatically for segmentation. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both subjective and objective tests. Meanwhile, the proposed method is applicable to industrial detection with limited computational resources.

show abstract

“…However, some interferences like stochastic industrial liquids increase the detection difficulty. Zhao et al [25] proposed a two-level labeling technique to solve the above problem based on superpixels. The pixels are clustered into superpixels and then superpixels are clustered into subregions, the superpixel boundaries are updated iteratively until pixels with similar visual senses are clustered into one superpixel, subregions after many rounds of growth will converge towards defects.…”

Section: ) Clusteringmentioning

confidence: 99%

“…Song et al [53] removed the edges of oil pollution interference and reflective pseudo-defect by fusing dilation and erosion operations into image subtraction operations. Further, this research team [25] utilized morphology subtraction to extracted defect edges from industrial liquid region on steel surface in the cold rolling process. With the firm and complete theory basis, mathematical morphology is widely used in nearly all aspects in image processing, including image segmentation, feature extraction, edge detection, image filtering, image enhancement, and so on.…”

Section: ) Morphologicalmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

304

111

View full text Add to dashboard Cite

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.

show abstract

Vision-based automatic detection of steel surface defects in the cold rolling process: considering the influence of industrial liquids and surface textures

Cited by 51 publications

References 17 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

Double Low-Rank and Sparse Decomposition for Surface Defect Segmentation of Steel Sheet

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Contact Info

Product

Resources

About