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Proceedings Paper

Surface defects detection of paper dish based on Mask R-CNN
Author(s): Xuelong Wang; Ying Gao II; Junyu Dong; Xukun Qin; Lin Qi; Hui Ma; Jun Liu
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Paper Abstract

Machine vision is widely used in the detection of surface defects in industrial products. However, traditional detection algorithms are usually specialized and cannot be generalized to detect all types of defects. Object detection algorithms based on deep learning have powerful learning ability and can identify various types of defects. This paper applied object detection algorithm to defects detection of paper dish. We first captured the images with different shapes of defects. Then defects in these images were annotated and integrated for model training. Next, the model Mask R-CNN were trained for defects detection. At last, we tested the model on different defects categories. Not only the category and the location of the defect in the image could be got, but also the pixel segmentation were given. The experiments show that Mask R-CNN is a successful approach for defect detection task, which can quickly detect defects with a high accuracy.

Paper Details

Date Published: 26 July 2018
PDF: 6 pages
Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 108280S (26 July 2018); doi: 10.1117/12.2502097
Show Author Affiliations
Xuelong Wang, Ocean Univ. of China (China)
Ying Gao II, Ocean Univ. of China (China)
Junyu Dong, Ocean Univ. of China (China)
Xukun Qin, Univ. of Minnesota Twin Cities (United States)
Lin Qi, Ocean Univ. of China (China)
Hui Ma, Ocean Univ. of China (China)
Jun Liu, Qingdao Agricultural Univ. (China)

Published in SPIE Proceedings Vol. 10828:
Third International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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