A new method in wheel hub surface defect detection: Object detection algorithm based on deep learning

In wheel hub surface defect detection, a unified image background is required. However, it is a challenging task because of the various categories of wheel hubs, and the complicated image background of the defect areas caused by the collection of the images with the defect areas in a narrow field of vision. Compared to the traditional method, the deep learning algorithm is more robust, which doesn't need the unified image background. We use Faster-RCNN with ResNet-101 as the object detection algorithm. And our related experiments show that our deep learning method is able to detect the scratches and points on the wheel hub in an image with a complicated background, as shown in Figure5. Furthermore, the model can detect defects on any part of the wheel hub of various types, and obtain the position and the class of the defective area. Particularly, the method achieves 86.3% mAP on our own data set.