Ensuring the reliability and high quality of electronic products necessitates accurate detection and classification of printed circuit board (PCB) defects. This study presents an enhanced defect detection model, TRSBi-YOLO. First, the incorporation of C3TR modules within the modified backbone structure improves multiscale feature extraction while significantly reducing computational complexity. Second, the integration of a simple parameter-free attention mechanism (SimAM) enhances feature representation without adding computational burden. Additionally, a bidirectional feature pyramid network (BiFPN) is employed in the neck section to optimize multi-level feature fusion and strengthen object detection capabilities. Experimental results demonstrate that the TRSBi-YOLO model achieves a remarkable mean average precision (mAP) of 98.1% with only 4.4G floating point operations (FLOPs) and 1.79M parameters. Compared to baseline model, TRSBi-YOLO shows substantial improvements, including a 2.40% increase in mAP, as well as enhancements in recall (0.62%), precision (0.73%), F1-score (1.12%), and accuracy (4.55%), along with a significant 11.99% increase in detection speed. These results demonstrate the effectiveness of the proposed TRSBi-YOLO model in improving both the detection and classification of PCB defects. The model offers a reliable and efficient solution for automated optical inspection (AOI), making it suitable for real-time quality control in industrial PCB manufacturing. Designed with deployment in mind, TRSBi-YOLO combines a lightweight architecture with high-speed performance, achieving an inference speed of 135.27 FPS. This makes it ideal for integration into high-throughput environments, including GPU-based systems and edge devices, supporting real-time and large-scale inspection tasks.