2023
DOI: 10.7717/peerj-cs.1264
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Adaptive visual detection of industrial product defects

Abstract: Visual inspection of the appearance defects on industrial products has always been a research hotspot pursued by industry and academia. Due to the lack of samples in the industrial defect dataset and the serious class imbalance, deep learning technology cannot be directly applied to industrial defect visual inspection to meet the real application needs. Transfer learning is a good choice to deal with insufficient samples. However, cross-dataset bias is unavoidable during simple knowledge transfer. We noticed t… Show more

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Cited by 3 publications
(1 citation statement)
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“…These defects or anomalies may include surface defects, cuts, cracks, deformations, size deviations, and other issues that could impact the quality and safety of the materials. Teaching a machine to automatically detect defects has become an emerging task in the computer vision area due to its vast potential applications in industrial production scenarios (Zhang et al 2023b;Lan and Huang 2023). Specifically, effective defect detection contributes to enhancing monitoring and control of product quality on the production line, which plays a crucial role in ensuring the products meet quality standards and reducing the rate of defective items.…”
Section: Introductionmentioning
confidence: 99%
“…These defects or anomalies may include surface defects, cuts, cracks, deformations, size deviations, and other issues that could impact the quality and safety of the materials. Teaching a machine to automatically detect defects has become an emerging task in the computer vision area due to its vast potential applications in industrial production scenarios (Zhang et al 2023b;Lan and Huang 2023). Specifically, effective defect detection contributes to enhancing monitoring and control of product quality on the production line, which plays a crucial role in ensuring the products meet quality standards and reducing the rate of defective items.…”
Section: Introductionmentioning
confidence: 99%