Automated metal surface inspection through machine vision

Wu, Wen‐Teng; Hou, Chengcheng

doi:10.1080/13682199.2003.11784415

Cited by 12 publications

(4 citation statements)

References 18 publications

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“…[9] introduced a similar mathematical model to extract relevant pixels corresponding to the presence of a discontinuity for crack recognition. Wu and Hu [10] suggested co-occurrence matrices of metal grayscale images to access the information of metal surfaces. Despite their quick runtime, these methods are highly dependent on the interference conditions and presence of noise.…”

Section: Related Workmentioning

confidence: 99%

Automatic Visual Inspection of Rare Defects: A Framework based on GP-WGAN and Enhanced Faster R-CNN

Jalayer,

Kaboli

et al. 2021

Preprint

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A current trend in industries such as semiconductors and foundry is to shift their visual inspection processes to Automatic Visual Inspection (AVI) systems, to reduce their costs, mistakes, and dependency on human experts. This paper proposes a two-staged fault diagnosis framework for AVI systems. In the first stage, a generation model is designed to synthesize new samples based on real samples. The proposed augmentation algorithm extracts objects from the real samples and blends them randomly, to generate new samples and enhance the performance of the image processor. In the second stage, an improved deep learning architecture based on Faster R-CNN, Feature Pyramid Network (FPN), and a Residual Network is proposed to perform object detection on the enhanced dataset. The performance of the algorithm is validated and evaluated on two multi-class datasets. The experimental results performed over a range of imbalance severities demonstrate the superiority of the proposed framework compared to other solutions.

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Section: Related Workmentioning

confidence: 99%

Automatic Visual Inspection of Rare Defects: A Framework based on GP-WGAN and Enhanced Faster R-CNN

Jalayer,

Kaboli

et al. 2021

Preprint

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Section: Introductionmentioning

confidence: 99%

“…The regular statistic methods are histogram method (Aminzadeh and Kurfess, 2015), gray-level co-occurrence matrix method (Wu and Hou, 2003) and local binary pattern method (Ko and Rheem, 2016), which are mainly used to measure the statistical characteristics of pixel spatial distribution. Wu and Hou (2003) has proposed a metal surface detection method built on the gray level co-occurrence matrix, first, they used the modified metal image gray level co-occurrence matrix to represent the metal surface information. Second, the gray level co-occurrence matrix difference and entropy are used as the characteristics of the metal surface, and lastly check the image characteristics and pre-set confidence interval to determine whether the metal surface exists defects.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization-based optimal real Gabor filter for surface inspection

Chu³

et al. 2019

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Purpose The traditional methods have difficulty to inspection various types of copper strips defects as inclusions, pits and delamination defects under uneven illumination. Therefore, this paper aims to propose an optimal real Gabor filter model for inspection; however, improper selection of Gabor parameters will cause the boundary between the defect and the background image to be not very clear. This will make the defect and the background cannot be completely separated. Design/methodology/approach The authors proposed an optimal Real Gabor filter model for inspection of copper surface defects under uneven illumination. This proposed method only requires a single filter by calculating the specific convolution energy of the Gabor filter with the image. The Real Gabor filter’s parameter is optimized by particle swarm optimization (PSO), which objective fitness function is maximization of the Gabor filter’s energy average divided by the energy standard deviation, the objective makes a distinction between the defect and normal area. Findings The authors have verified the effect with different iterations of parameter optimization using PSO, the effects with different control constant of energy and neighborhood window size of real Gabor filter, the experimental results on a number of metal surface have shown the proposed method achieved a well performance in defect recognition of metal surface. Originality/value The authors propose a defect detection method based on particle swarm optimization for single Gabor filter parameters optimization. This proposed method only requires a single filter and finds the best parameters of the Gabor filter. By calculating the specific convolution energy of the Gabor filter and the image, to obtain the best Gabor filter parameters and to highlight the defects, the particle swarm optimization algorithm’s fitness objective function is maximize the Gabor filter's average energy divided by the energy standard deviation.

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“…Zheng et al [32] proposed an automatic inspection method of metallic surface defects, in which they employed genetic algorithms to automatically learn morphology processing parameters. Wu and Hou [29] proposed an automated visual inspection method for inspecting metal surfaces. They used the modified grey-level co-occurrence matrices of metal images to access the information of metal surfaces.…”

Section: Introductionmentioning

confidence: 99%