Surface defects detection of paper dish based on Mask R-CNN

Xuelong Wang; Ying Gao II; Junyu Dong; Xukun Qin; Lin Qi; Hui Ma; Jun Liu

doi:10.1117/12.2502097

26 July 2018 Surface defects detection of paper dish based on Mask R-CNN

Xuelong Wang, Ying Gao II, Junyu Dong, Xukun Qin, Lin Qi, Hui Ma, Jun Liu

Proceedings Volume 10828, Third International Workshop on Pattern Recognition; 108280S (2018) https://doi.org/10.1117/12.2502097
Event: Third International Workshop on Pattern Recognition, 2018, Jinan, China

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.

Citation Download Citation

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); https://doi.org/10.1117/12.2502097

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