The detection of surface defects on a printed circuit board (PCB) plays a crucial role in ensuring the quality of PCB products. To address the diversity of defect poses and the challenges associated with detecting small objects on PCB, this paper proposes an improved YOLOv7 model for small objects-oriented PCB defect detection. First, this paper improves the regression loss function of YOLOv7 by incorporating wise-IoU (WIoU) and replacing IoU with an outlier degree to develop a gradient-boosting allocation strategy, thereby increasing the network’s accuracy. Second, this paper proposes a coordinate attention dynamic mechanism that performs convolution operations with deformable convolutional networks v2 (DCNv2) using coordinate attention. This mechanism effectively suppresses redundant information. Finally, this paper proposes a dynamic head diverse (DyHead-d) module that prioritizes spatial awareness over scale awareness, building on DyHead. This module improves the network’s ability to localize small targets. Experimental results show that the WDC-YOLO achieves a mean average precision of 98.4% on public datasets, demonstrating a 3.1% improvement compared to the original network. The significantly enhanced detection accuracy meets the real-time requirements of PCB defect diagnosis, which is of great importance for quality control and cost reduction in PCB industrial production.