Workpiece surface defect detection method based on deep learning

The invention discloses a workpiece surface defect detection method based on deep learning. The method specifically comprises: collecting workpiece images under different backgrounds and illuminationconditions; preprocessing the acquired workpiece image; constructing a deep convolutional neural network model to obtain feature maps of six different layers; carrying out multi-scale feature fusion prediction by adopting a feature pyramid feature map, obtaining and generating four anchor box prediction target bounding boxes by using a K-means clustering algorithm, and predicting categories by using a cross entropy loss function; removing a redundant prediction bounding box through a non-maximum suppression algorithm; and outputting the position information and types of the workpiece surface defects. The method solves the problems of low detection efficiency and poor precision of manual detection and physical detection methods, overcomes the problem of poor adaptability of traditional machine vision defect detecti