CSS-YOLOv8: an efficient detection model for printed circuit boards with tiny defects

Abstract Printed circuit boards (PCBs) with tiny defect detection face the problems of frequent omission and false detection, which seriously affect the reliability and safety of electronic products. To address these problems, a highly accurate and advanced PCB with a tiny defect detection model was proposed. This model incorporates four innovations. First, a small target detection head is augmented to capture more features for PCB with tiny defects. Second, the content-aware reassembly of features (CARAFE) operator is introduced to accumulate semantic information and local features. Third, a simple, parameter-free attention module (SimAM) is integrated into the C2f module to form the C2f-SimAM module, thereby strengthening the acquisition of channel and spatial information and enabling easier perception of tiny defects. Finally, space-to-depth with a non-strided convolution (SPD-Conv) module is used to dramatically reduce the loss of the feature map content. Therefore, this model is named CSS-YOLOv8. The results of this study confirm that the CSS-YOLOv8 model obtained a recall (R) of 95.5% with a mean average precision (mAP) of 97.9% on the PCB-DATASET dataset. After that, the CSS-YOLOv8 model had 7.2% and 5.7% improvements in R and mAP, respectively, compared to the original model. Accordingly, the CSS-YOLOv8 model significantly reinforces the accuracy of tiny defect detection in PCBs, and alleviates the omission and false detection of PCBs with tiny defects.