Online Detection Technique of 3D Defects for Steel Strips Based on Photometric Stereo

Wang, Lei; Xu, Ke; Zhou, Peng

doi:10.1109/icmtma.2016.109

Cited by 15 publications

(5 citation statements)

References 9 publications

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“…A trio of authors, Wang et al [ 17 ], developed an online method of detecting 3D defects based on the photometric stereo. This enabled them to accurately identify and locate 3D defects in sheet steel.…”

Section: Introductionmentioning

confidence: 99%

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

et al. 2021

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This paper describes the enhancement of the existing predictive system of quality management in the processes of metallurgic manufacturing. Specifically, it addresses steel-strip manufacturing. The main quality management innovation is the transition from the current methodological process of a single-step defect evaluation to a two-step evaluation. A two-step defect check of the strip’s surface involves checking for defects during the hot-rolling process first, and double-checking it during the process of pickling. These defects are detected in a well-established process of camera imaging in the production process. The recorded image is then processed mathematically to find the degree of defect correlation in those processes. The two-step evaluation enables a more detailed focus on a particular defect and its position on the strip. Decisions concerning further processing are based on defect evaluation, for instance, whether a rework is necessary to maximize the product utilization and minimize the eventual negative impact of the defect on production equipment. A crucial aspect is also the reduced probability of failures in the manufacturing process.

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Section: Introductionmentioning

confidence: 99%

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

et al. 2021

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“…In contrast, * Author to whom any correspondence should be addressed. three-dimensional (3D) visual inspection techniques [7,8] have become a major focus of research in surface defect detection. Among these 3D methods, structured light technology [9] has garnered significant attention for its non-contact approach, fast processing speed, high operational efficiency, and wide inspection range.…”

Section: Introductionmentioning

confidence: 99%

Depth extraction of 3D defects on curved surfaces with multi-line lasers

Jin,

Li,

2024

Meas. Sci. Technol.

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The adoption of computer vision technology has significantly impacted surface defect inspection by providing a non-contact, cost-effective solution that has been widely accepted. Among the various techniques available, three-dimensional (3D) defect inspection using multi-line lasers is notable for its simplicity, high detection speed, and extensive coverage. The accuracy of this method is significantly constrained by the precision of laser stripe extraction. In industrial environments, achieving accurate extraction is hindered by the intricate surface geometries of objects and the challenge of maintaining uniform brightness in multi-line laser stripes. To address these challenges, we propose a novel approach to extract the depth of 3D defects on surfaces using multi-line lasers. Our method combines guided filtering and the Frankle-McCann Retinex algorithms to improve the quality of captured images. We have refined the laser stripe extraction process and proposed an advanced adaptive threshold segmentation technique that utilizes the OTSU method to determine threshold coefficients, followed by secondary segmentation based on a neighborhood search. The extracted laser strips are then processed using the quadratic weighted gray gravity method. Additionally, we proposed an innovative region-growth segmentation method based on neighborhood search that effectively segments individual laser strips. We also design a strategy for determining 3D defect depths in situations where precise camera calibration is challenging. The efficacy of our proposed method was rigorously tested on a hot-rolled seamless steel tube with a diameter of 145 mm. The resulting 3D defect depth exhibited an error of less than 0.5 mm, meeting the stringent standards required for practical applications.

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“…Both defect detection methods and photometric stereo methods achieve outstanding performance on their respective benchmark. There are also methods [ 8 , 9 , 40 , 41 ] that combine photometric stereo with defect detection. Ren et al [ 42 ] used a data-driven PS method to extract the surface normal and separate defects from the background through filters.…”

Section: Introductionmentioning

confidence: 99%

Photometric-Stereo-Based Defect Detection System for Metal Parts

Cao

Ding

Chen

et al. 2022

Sensors

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Automated inspection technology based on computer vision is now widely used in the manufacturing industry with high speed and accuracy. However, metal parts always appear in high gloss or shadow on the surface, resulting in the overexposure of the captured images. It is necessary to adjust the light direction and view to keep defects out of overexposure and shadow areas. However, it is too tedious to adjust the position of the light direction and view the variety of parts’ geometries. To address this problem, we design a photometric-stereo-based defect detection system (PSBDDS), which combines the photometric stereo with defect detection to eliminate the interference of highlights and shadows. Based on the PSBDDS, we introduce a photometric-stereo-based defect detection framework, which takes images captured in multiple directional lights as input and obtains the normal map through the photometric stereo model. Then, the detection model uses the normal map as input to locate and classify defects. Existing learning-based photometric stereo methods and defect detection methods have achieved good performance in their respective fields. However, photometric stereo datasets and defect detection datasets are not sufficient for training and testing photometric-stereo-based defect detection methods, thus we create a photometric stereo defect detection (PSDD) dataset using our PSBDDS to eliminate gaps between learning-based photometric stereo and defect detection methods. Furthermore, experimental results prove the effectiveness of the proposed PSBBD and PSDD dataset.

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Online Detection Technique of 3D Defects for Steel Strips Based on Photometric Stereo

Cited by 15 publications

References 9 publications

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

Depth extraction of 3D defects on curved surfaces with multi-line lasers

Photometric-Stereo-Based Defect Detection System for Metal Parts

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