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Printed Circuit Board (PCB) defect detection is crucial for ensuring the quality and reliability of electronic devices. The study proposes an enhanced YOLOv5s model for PCB defect detection, which combines Coordinate Attention (CA), Convolutional Block Attention Module (CBAM), and Inception-style convolutions (IO). This model aims to improve the detection accuracy of small defects while reducing computational complexity. Experiments on the PCB defect dataset demonstrate that the proposed CA-CBAM-IOYOLOv5s model achieves higher accuracy (97.8%), recall (98.6%), and F1 score (98.3%) compared to the basic YOLOv5s and other state-of-the-art models. The model also shows excellent performance in detecting various types of PCB defects, with an average detection accuracy of 98.45% and an average detection time of 0.114 seconds. These results indicate that the proposed model provides a promising solution for efficient and accurate PCB defect detection in industrial applications.