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Automated Surface Defects Detection on Mirrorlike Materials by using Faster R-CNN

Automated surface defects detection based on machine learning has become a fascinating and promising area of research, with a great direct impact on different visual inspection applications. Different deep-learning methods come with worthy approaches to deal with this task. However, the detection of surface anomalies on mirror-like materials still is a challenge in manufacturing scenarios. Recently, traditional methods are utilized to detect surface defects. However, their algorithms depend on handcraft extraction of features and require to work under specific illumination conditions, making their defects recognition ability to be limited. This paper proposes a faster RCNN model to improve the automation of surface defects detection on mirrorlike materials. The model adopts VGG16 architecture as a feature extractor to make sure that the filters and resulting feature map capture useful features. Six different types of surface defects named as crack, microcrack, pinhole, dirt, pit, and spots were evaluated on a ceramic close stool as a research object. Experimental results have shown that the model achieved the best performances compared with the traditional method by reaching a detection accuracy of 94%.