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A fast and robust convolutional neural network-based defect detection model in product quality control

Abstract : The fast and robust automated quality visual inspection has received increasing attention in the product quality control for production efficiency. To effectively detect defects in products, many methods focus on the hand-crafted optical features. However, these methods tend to only work well under specified conditions and have many requirements for the input. So the work in this paper targets on building a deep model to solve this problem. The elaborately designed deep convolutional neural networks (CNN) proposed by us can automatically extract powerful features with less prior knowledge about the images for defect detection, while at the same time is robust to noise. We experimentally evaluate this CNN model on a benchmark dataset and achieve a fast detection result with a high accuracy, surpassing the state-of-the-art methods.
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https://hal-utt.archives-ouvertes.fr/hal-03320589
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Monday, August 16, 2021 - 10:51:31 AM
Last modification on : Wednesday, October 13, 2021 - 7:16:03 PM

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Tian Wang, Yang Chen, Meina Qiao, Hichem Snoussi. A fast and robust convolutional neural network-based defect detection model in product quality control. International Journal of Advanced Manufacturing Technology, Springer Verlag, 2018, 94 (9-12), pp.3465-3471. ⟨10.1007/s00170-017-0882-0⟩. ⟨hal-03320589⟩

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