## 1 Introduction  
The production of high-performance PCBs involves complex technologies and manufacturing processes, which often lead to defects such as short circuits and open circuits seriously affecting the performance and lifespan of electronic products. Therefore, developing efficient and low miss detection rate PCB defect detection technology is particularly crucial. Indeed, PCB defect detection still faces the following challenges: (1) most defects on PCBs are small targets that are hard to identify by the human eye; (2) semantic information of photo images is limited; and (3) the existing PCB samples are poorly targeted. Traditional PCB defect detection relies on manual visual inspection [1], optical inspection [2], and electrical inspection [3]. However, while manual visual inspection is low-cost, it is prone to subjective factors, inefficient, and struggles to identify microscopic defects. Optical inspection, on the other hand, offers speed and accuracy but comes with the drawback of expensive equipment and complex maintenance. Electrical inspection, being cost-effective and capable of detecting electrical performance defects, is constrained by the complexity of test fixtures, programming, and debugging, and has limited recognition of non-electrical defects. Therefore, deep learning-based PCB defect detection, with its high precision, efficiency, and scalability, has significantly improved the accuracy and speed of detection, making it an indispensable advanced detection technology in modern electronics manufacturing. In recent years, Deep Learning (DL) has attracted much attention due to its ability in feature extraction and generalization, becoming a promising choice for target defect detection. Currently, DL-based object detection algorithms are mainly divided into two categories: two-stage detection algorithms [4] and one-stage detection algorithms [5]. Two-stage detection algorithms adopt a two-step strategy, which first generates candidate regions and then classifies these regions. They are renowned for their high detection accuracy and low miss rate, but suffer from a slow detection speed, computational complexity, and low timeliness. In contrast, one-stage detection algorithms adopt an integrated strategy that combines feature extraction from candidate regions with the localization of predicted bounding boxes, directly classifying and locating objects [6]. Typical algorithms include \"You Only Look Once\" (YOLO) series algorithms (YOLOv3, YOLOv4, YOLOv5). They are characterized by a fast detection speed and low computational complexity, meeting the demand for real-time detection. To further improve detection accuracy, [PERSON] [7] extended the original YOLOv3 architecture by combining virtual and real PCB image datasets, and improved detection accuracy by adding additional output layers to the original YOLOv3 architecture. However, this method has limited accuracy in identifying small-target defects and fails to achieve a balance between speed and precision. [PERSON] [8] proposed GSC-YOLOv5, a DL-based detection method combining a lightweight network and dual attention mechanisms to effectively address small-target detection issues; however, the attention mechanism introduced was complex and slow. [PERSON] [9] introduced contextual attention into the YOLOv5s model, significantly reducing missed and false detections in small targets and complex background textures by implementing compression training methods and integrating micro detection heads. Although this method improves accuracy, its compression method leads to a longer training time, which affects the efficiency of the model. Addressing the problems of low efficiency and accuracy in PCB defect classification technology, [PERSON] [10] proposed using YOLOv8 for PCB defect detection, which significantly improved the prediction accuracy of the model. However, this method does not achieve a balance between accuracy and real-time performance, and its performance in resource-constrained scenarios is not satisfactory.