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26 July 2018 Surface defects detection of paper dish based on Mask R-CNN
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Proceedings Volume 10828, Third International Workshop on Pattern Recognition; 108280S (2018)
Event: Third International Workshop on Pattern Recognition, 2018, Jinan, China
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.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xuelong Wang, Ying Gao II, Junyu Dong, Xukun Qin, Lin Qi, Hui Ma, and Jun Liu "Surface defects detection of paper dish based on Mask R-CNN", Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 108280S (26 July 2018);


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