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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Acknowledgements
This work is partially supported by the ANR AutoFerm project and the Platform CAPSEC funded by Région Champagne-Ardenne and FEDER, the Fundamental Research Funds for the Central Universities (YWF-14-RSC-102), the National Natural Science Foundation of China (U1435220, 61503017), the Aeronautical Science Foundation of China (2016ZC51022).
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Wang, T., Chen, Y., Qiao, M. et al. A fast and robust convolutional neural network-based defect detection model in product quality control. Int J Adv Manuf Technol 94, 3465–3471 (2018). https://doi.org/10.1007/s00170-017-0882-0
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DOI: https://doi.org/10.1007/s00170-017-0882-0