In view of the issue of balancing detection accuracy with the number of parameters and computational load in printed circuit board (PCB) defect detection, this study proposes a lightweight PCB defect detection algorithm based on ADS-YOLOv8n. Firstly, the ADown downsampling module is introduced to retain more detailed defect information and enhance the ability to extract detail defects. Secondly, a DTFM module incorporating three layers features is designed to enhance feature extraction and ability to localize defects. Then, a new SCM module is designed to enhance the focus on defect information. Finally, the WIoUv3 bounding box loss function is introduced to enable the model to obtain more accurate regression results. The mean average precision of the improved model reaches 98.43% and the recall rate reaches 96.58%, compared with the benchmark model, the mean average precision is improved by 3.20 percentage points, the recall rate is improved by 5.17 percentage points, and the number of parameters and computation volume are reduced by 5.0×10⁵ and 3.0×10⁸, respectively. The improved model takes into account the lightweight of the model on the basis of improving the detection precision.