The work aims to apply YOLOv5 algorithm to defect detection of bare PCB, so as to improve detection accuracy. Feature fusion path was added to directly connect layers C2, C3 and C4 with layers P2, P3 and P4, so as to reduce the loss of information. Shallower C2, F2 and P2 feature images were introduced to increase the details of the image. Moreover, the attention mechanism SE_block was used to improve the accuracy of the original algorithm. The average accuracy of the improved network increased from 91.54% to 97.36%, with a growth of 5.82%. For all kinds of defects, the algorithm could keep a detection accuracy above 90%, which met the needs of industry. The proposed algorithm improves the detection accuracy, reflects the role of shallow information in small target detection, verifies the advantages of multi-information fusion pathway, and highlights the advantages of attention mechanism. Compared with the original algorithm, the proposed algorithm has certain advantages.