Aiming at the problem of small target and low detection accuracy of printed circuit board surface defects, Multi-CR YOLO, a printed circuit board surface defect detection network, is designed to meet the premise of real-time detection speed and effectively improve the detection accuracy. Firstly, the backbone feature extraction network Multi-CR backbone, which consists of three Multi-CR residual blocks, performs feature extraction for small target defects on printed circuit boards. Secondly, the SDDT-FPN feature fusion module enables the feature fusion from the high level feature layer to the low level feature layer, and at the same time strengthens the feature fusion for the feature fusion layer where the small target prediction head YOLO Head-P3 is located, to further enhance the expressive ability of the low level feature layer. The PCR module strengthens the feature fusion mechanism of the different scales of the backbone feature extraction network and the feature layer of the SDDT-FPN feature fusion module, and prevents the fusion mechanism between the modules. The C5ECA module is responsible for adaptively adjusting the feature weights and adaptively paying attention to the requirement of small target defect information, which further improves the adaptive feature extraction capability of the feature fusion module. Finally, the three YOLO-Head are responsible for predicting small target defects for different scales. The experiments show that the detection mAP of the Multi-CR YOLO network model reaches 98. 55%, the model size is 8. 90 MB, which meets the lightweight requirement, and the detection speed reaches 95. 85 fps, which meets the application requirements of real-time detection of small-target defects.