High-quality printed circuit boards (PCBs) are essential components in modern electronic circuits. Nevertheless, most of the existing methods for PCB surface defect detection neglect the fact that PCB surface defects in complex backgrounds are prone to long-tailed data distributions, which in turn affects the effectiveness of defect detection. Additionally, most of the existing methods ignore the intra-scale features of defects and do not utilize auxiliary supervision strategies to improve the detection performance of the network. To tackle these issues, we propose a lightweight long-tailed data mining network (LLM-Net) for identifying PCB surface defects. Firstly, the proposed Efficient Feature Fusion Network (EFFNet) is applied to embed intra-scale feature associations and multi-scale features of defects into LLM-Net. Next, an auxiliary supervision method with a soft label assignment strategy is designed to help LLM-Net learn more accurate defect features. Finally, the issue of inadequate tail data detection is addressed by employing the devised Binary Cross-Entropy Loss Rank Mining method (BCE-LRM) to identify challenging samples. The performance of LLM-Net was evaluated on a homemade dataset of PCB surface soldering defects, and the results show that LLM-Net achieves the best accuracy of mAP@0.5 for the evaluation metric of the COCO dataset, and it has a real-time inference speed of 188 frames per second (FPS).

高质量印刷电路板 (PCB) 是现代电子电路的重要组成部分。然而，现有的PCB表面缺陷检测方法大多忽略了复杂背景下PCB表面缺陷容易出现长尾数据分布的事实，进而影响缺陷检测的有效性。此外，大多数现有方法忽略了缺陷的尺度内特征，并且没有利用辅助监督策略来提高网络的检测性能。为了解决这些问题，我们提出了一种轻量级长尾数据挖掘网络（LLM-Net）来识别 PCB 表面缺陷。首先，应用所提出的高效特征融合网络（EFFNet）将缺陷的尺度内特征关联和多尺度特征嵌入到LLM-Net中。接下来，设计了一种具有软标签分配策略的辅助监督方法，以帮助LLM-Net学习更准确的缺陷特征。最后，通过采用设计的二元交叉熵损失排名挖掘方法（BCE-LRM）来识别具有挑战性的样本，解决了尾部数据检测不足的问题。在自制的 PCB 表面焊接缺陷数据集上对 LLM-Net 的性能进行了评估，结果表明 LLM-Net 的评估指标达到了 mAP@0.5 的最佳精度COCO数据集的实时推理速度为每秒188帧（FPS）。