Accurate detection and classification of mixed defects in printed circuit board (PCB) are critical for maintaining the quality and reliability of electronic product. This paper presents TR-YOLO, an enhanced version of the YOLOv5 model, which is developed for PCB defect inspection. TR-YOLO introduces a modified backbone structure incorporating a C3TR module, enabling effective extracting features at multiple scales while minimizing computational resources overhead. The findings from the experiments indicate that TR-YOLO attains a mean average precision (mAP) of 96.4%, with 4.1 giga FLOPs and 1.80 million parameters. Compared to the YOLOv5 model, the proposed TR-YOLO improves mAP by 0.63%, precision by 0.73%, recall by 1.04%, F1-score by 1.34%, and overall accuracy by 0.17%. Moreover, it reduces computational complexity by 2.38% (i.e., reduced FLOPs) and enhances the speed by 7.61%. These findings highlight TR-YOLO’s ability to deliver a balanced performance between detection accuracy and processing efficiency. The proposed model shows strong potential for real-time automatic optical inspection (AOI) in PCB manufacturing, contributing to improved product quality and production reliability.