The paper proposes a YOLOPCB network for small defects detection on printed circuit board ( PCB) using multi-channel feature fusion learning. Firstly, the last group of MPConv layer and E-ELAN layer in the YOLOv7 backbone network are removed, and the ECU module in the fusion layer and the 20 × 20 prediction head are eliminated. A cross-channel information connection module (CIC) is utilized to link the streamlined backbone and fusion networks. Secondly, a shallow feature fusion module ( SFF) and a new anchor matching strategy are designed, which add two low-level, high-resolution detection heads. Lastly, the three E-ELAN layers in the YOLOv7 backbone network are used as inputs, while the bottommost E-ELAN and two concatenation modules in the fusion layer are used as outputs, with adaptive weighted skip-connection (AWS) to increase the information within the same dimension. The average precision on the PCB Defect datasets reaches 94. 9% , with a detection speed of 45. 6 fps. Furthermore, on the Self-PCB datasets obtained from on-site enterprises, YOLOPCB achieves the highest accuracy of 76. 7% , which is a 6. 8% improvement over the detection accuracy of YOLOv7. YOLOPCB effectively enhances the detection capability of small defects on printed circuit boards.