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The emergence of machine vision has promoted the automation of defect detection (DD) in the industrial field. Therefore, scholars at home and abroad have carried out a lot of research and exploration on the traditional visual DD method of mechanical design products. At the same time, this method has been widely used in the field of modern manufacturing due to its noncontact and fast detection speed. The traditional visual detection method is to use cameras, computers, and other equipment instead of people to detect the detected objects, although this method improves the production efficiency to a certain extent. However, this detection method is greatly affected by light, has a certain false detection rate, and has poor adaptability. The intelligent detection method based on deep learning developed on the basis of traditional vision is a further optimization of traditional visual detection methods. The rapid development of deep learning makes the advantages of visual DD more obvious.

Defect Detection for Mechanical Design Products with Faster R-CNN Network