Defect Detection Method of Castings Based on Deep Learning

Aiming at the problems of difficulty in obtaining X-ray images of castings, low efficiency of manual and traditional image recognition methods and high rate of missed judgments, a casting defect detection method is proposed in this paper based on deep learning. Firstly, the overlap data augmentation method is used to achieve defect augmentation and the image complexity is further improved based on simplified Mosaic data augmentation; then the defect detection model is constructed based on the idea of YOLO (you only look once); finally, a test image defect detection method based on bounding box suppression is proposed, which iteratively completes the defect detection in the test image. The experimental results show that the automatic detection of various casting defects can be effectively realized with this method, which provides a solution based on deep learning to the defect detection of castings.