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- W3005200228 abstract "Surface defect detection of industrial products has always been an important part of the manufacturing industry. At present，there is a high false detection rate and low efficiency problem of traditional image processing algorithms which easy to be disturbed by complex background. Aiming at the above problems, a method for surface defect detection based on deep learning is proposed. YOLOv3 network adopted in this paper has great advantages in small target recognition and location of target in complex background. In addition, the train-set is effectively extended by elastic deformation and thin-plate spline algorithm. The experiment results show that the scratch recognition rate is as high as 95.8%, the over-judgment rate is 5.4%,and the missed rate is 1.3%.The method can identify the surface defects in a short time, and the average detection time does not exceed 0.4s, which can meet the real-time and precision requirements of industrial applications." @default.
- W3005200228 created "2020-02-14" @default.
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- W3005200228 date "2019-10-01" @default.
- W3005200228 modified "2023-09-24" @default.
- W3005200228 title "Deep Learning: Excellent Method at Surface Defect Detection of Industrial Products" @default.
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- W3005200228 doi "https://doi.org/10.1109/imcec46724.2019.8984019" @default.
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