To resolve the current stage of printed circuit board （PCB） defect detection， it is necessary to consider both the detail and global information of the defects simultaneously. The downsampling operation of cross-pixel convolution or pooling results in the loss of both global and detailed information on the surface defects of printed circuit boards （PCBs）. Although some of the methods above employ attention mechanisms for intra-layer information， the issue of insufficient attention to the weight bias problem resulting from conventional convolution after feature extraction persists. The PCB defect detection Network （PCBNet） proposed in this paper employed the inflated Dilation and extrusion convolution （DeConv） to extract both global and detailed information about PCB surface defects. Downsampling was performed using Spatial Passage Directed Focused Convolution （SPD-Conv） to minimize the loss of information. The Subtle Feature Enhancement Module （SFEM） had been designed to adjust the intra-layer relationship of PCB surface defect features and reduce the weight bias while enhancing the algorithm's ability to perceive the subtle features. The experimental results obtained by comparing the PCB surface soldering defects dataset and the PCB Defect-Augmented dataset， which were collected in the field using multiple state-of-the-art methods， demonstrate that PCBNet is not only capable of accurately identifying PCB surface soldering defects at a rate of 83 frames per second on the PCB surface soldering defects dataset but also achieves the following results on the PCB Defect-Augmented dataset： the highest accuracy of mAP_0.5， which is the evaluation metric of the COCO dataset. This indicates that our method has the potential to be implemented on embedded devices.