Abstract: The surfaces of Printed Circuit Boards (PCB) feature complex textures with defects that are small as well as diverse in type and shape. Typically, accurate detection of small targets necessities the addition of detection heads at smaller scales, which significantly increases the computational cost and reduces the detection speed. To address this issue, this study proposes PCB-Det, a multi-scale feature fusion learning model for small target defect detection on a PCB. Building on the YOLOv8 architecture, PCB-Det replaces the original backbone network with a lightweight backbone, PP-HGNet, and incorporates a GSPPFCSPC module for multi-level feature extraction, expanding the receptive field to enrich feature information. Meanwhile, a Progressive Bidirectional Feature Pyramid Network (Pro-BiFPN) multi-scale feature fusion network is designed to enhance the interactions between adjacent feature layers, optimizing the fusion of shallow detail information and deep semantic information. In addition, the model employs a shared feature branch to reduce the weight of the original detection head and introduces the Wise-IoU loss function, which dynamically adjusts loss weights to accelerate model convergence. The experimental results show that PCB-Det achieves an Average Precision (AP) of 98.1% on PCB_DATASET, representing a 3.5 percentage point improvement over the baseline model, effectively reducing missed detections and false alarms and enhancing the detection capability for small target PCB defects.

Key words: Printed Circuit Board (PCB), small target detection, multi-scale feature fusion, receptive field, defect detection

摘要： 印制电路板（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，导致计算成本大幅提高，检测速度减慢。针对这一问题，提出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架构的基础上，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（AP）达到98.1%，相比基准模型提升了3.5百分点，有效减少了漏检与误检，提高了PCB小目标缺陷的检测能力。

关键词: 印制电路板, 小目标检测, 多尺度特征融合, 感受野, 缺陷检测

CLC Number: 

• TP391