Abstract: Surface defect of workpiece is an important factor that affects the performance of mechanical equipment. Fast and efficient detection is the focus of current research. In order to solve the problem of workpiece surface defect detection, a detection method based on SSD model is proposed. By proposing DH-Mobilenet network to replace VGG16 network in SSD structure, this method simplifies the detection model and reduces the computation. At the same time, the inverse residual block is used to predict the position, and the dilated convolution is used to replace the down sampling operation to avoid information loss. Scanning electron microscope is used to obtain the surface image of workpiece, and the workpiece surface defect data set is established and expanded. Finally, three kinds of high frequency defects, namely fragment, peeling off and pear ditch, are trained and tested, and the results are compared with the original models of YOLO, Faster R-CNN and SSD. The test results show that this method can detect the surface defects of the workpiece more accurately and quickly, which provides a new idea for the defect detection in the actual industrial scene.

Key words: workpiece defect, SSD model, MobileNet, object detection