摘要： 印制电路板（Printed Circuit Board, PCB）表面纹理复杂、缺陷尺寸小且种类繁多、形状各异，为准确检测小目标，通常需要添加更小尺度的检测头，导致计算成本大大提高，检测速度减慢。针对这一问题，提出了一种多尺度特征融合学习的PCB小目标缺陷检测模型PCB-Det。该模型在YOLOv8架构基础上，采用轻量级主干网络PP-HGNet替代原有主干网络，并结合GSPPFCSPC模块进行多层级特征提取，拓展感受野以丰富特征信息。同时，设计了Pro-BiFPN多尺度特征融合网络，通过增强相邻层特征之间的交互，优化浅层细节信息和深层语义信息的融合效果。此外，模型还使用共享特征分支对原有检测头进行轻量化改进，并引入Wise-IoU损失函数，动态调整损失权重，加速模型收敛。实验结果表明，PCB-Det在PCB_DATASET缺陷数据集上的平均精度达到98.1%，相比基准模型提升了3.5%，有效减少了漏检与误检，提高了PCB小目标缺陷的检测能力。

Abstract: The surface texture of a printed circuit board (PCB) is complex, with defects that are small and come in a variety of shapes. In order to accurately detect small targets, smaller-scale detection heads are often added, which has the effect of significantly increasing the computational cost and slowing down the detection speed. To address this issue, we propose a multi-scale feature fusion learning model for PCB small-target defect detection, named PCB-Det. Based on the YOLOv8 architecture, the model replaces the original backbone network with the lightweight PP-HGNet and incorporates the GSPPFCSPC module for multi-level feature extraction, thereby expanding the receptive field to enrich feature information. Furthermore, we have devised the Pro-BiFPN feature fusion network with the objective of enhancing the interaction between features from adjacent layers, thereby optimizing the fusion of shallow detail information and deep semantic information. Furthermore, the model incorporates shared feature branches to reduce the computational burden of the original detection heads and employs the Wise-IoU loss function to dynamically adjust the loss weights, thereby accelerating model convergence. The experimental results demonstrate that the proposed PCB-Det model achieves an average precision of 97.7% on the PCB_DATASET defect dataset, representing a 3.1% improvement over the baseline model. The model effectively reduces both missed detections and false positives, thereby enhancing the detection capability for small-target defects in PCBs.