Abstract
With the continuous development of artificial intelligence, great progress has been made in the field of object detection. Defect detection is a branch of the field of object detection, as long as the purpose is to locate and classify defects on the surface of objects to help people further analyze product quality. In large-scale manufacturing, the demand for product surface defect detection has always been strong, and companies hope to reduce costs, while improving detection accuracy. This paper mainly proposes a method that is biased towards the detection of surface defects on smooth products, solving the problems including difficulty on detecting small scratches and imbalance between positive and negative samples. Finally, we achieve good results through the detector.
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Acknowledgements
This research was supported by Defense Industrial Technology Development Program under Grant No. JCKY2016605B006, Six talent peaks project in Jiangsu Province under Grant No. XYDXXJS-031.
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Sun, Y., Cai, Y., Li, Y., Zhao, Y. (2020). Defect Detection of Production Surface Based on CNN. In: Yu, Z., Becker, C., Xing, G. (eds) Green, Pervasive, and Cloud Computing. GPC 2020. Lecture Notes in Computer Science(), vol 12398. Springer, Cham. https://doi.org/10.1007/978-3-030-64243-3_30
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DOI: https://doi.org/10.1007/978-3-030-64243-3_30
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