To address the issues in printed circuit board(PCB)surface defect detection networks, such as low sensitivity to small defects and low detection efficiency, a PCB defect detection algorithm CID-YOLO based on the improved YOLOv11 is proposed. First, the C3k2 module is improved by integrating Inception Depthwise Convolution, which, through a multi-branch feature extraction mechanism, reduces the loss of high-level feature information of PCB surface defects caused by increased network depth and decreases the number of model parameters. Second, a dynamic activation mechanism, Dynamic Tanh, is introduced to improve the C2PSA module, enhancing the model′s nonlinear representation capability, allowing it to better adapt to defect features of different scales. Next, the CGBD downsampling module is embedded to improve the recognition capability for special PCB surface defects through an efficient context information extraction mechanism. Finally, the original classification loss function is replaced, and a dynamic weighting strategy is applied to suppress gradient bias caused by data imbalance, effectively improving model generalization performance. Experimental results show that the proposed CID-YOLO model achieves mAP50 and mAP50.95 scores of 96.0% and 52.7%, respectively, on the Peking University public PCB defect dataset, improving 2.4% and 1.6% compared to the baseline model, demonstrating the effectiveness of the proposed algorithm.