MSD-YOLO: A Novel Method for Detecting Microscopic Surface Defects in Metal spray-painted thermal mugs

VMetal spray-painted thermal mugs, as a typical industrial product, are produced on a large scale worldwide. However, the problem of detecting microscopic surface flaws, which leads to an increased rate of returns and a steep drop in customer satisfaction, represents a common challenge in the field of industrial product surface defect detection. This paper introduces a novel microscopic surface defect detection network named MSD-YOLO (Microscopic Surface Defects-YOLO). This model is specifically targeted at microscopic surface defect detection through the innovative MGC2f (Multiscale-Group Convolution to Feature) module and its integration with the Inner-IoU method. And the method optimizes multi-scale convolutional kernels and designs a new loss function, enabling the detection of microscopic defects in complex industrial product images. Experimental results indicate that the MSD-YOLO model improves the mAP (mean Average Precision) and F1 score by 5.492% and 5% respectively, compared to the original YOLOv8 model, significantly enhancing the model's detection performance. Additionally, this study provides a dataset of surface defects on metal spray-painted thermal mugs, enriching the data in the field of industrial product surface defects.