Accurate and efficient PCB defect detection is a key component of automated quality inspection in electronic manufacturing. In recent years, with the rapid development of deep learning, surface defect detection technology has made significant progress. However, the small size, diverse morphology, and complex background of PCB defects still pose significant challenges for accurate detection. To address these issues, this paper proposes a PCB surface defect detection network (MPHF-Net) based on transformer multi-dimensional perception and high-low frequency fusion. First, this paper proposes a cross stage partial multi-scale edge enhancement module to enhance multi-scale feature extraction capabilities and improve the representation of defect edge details. Second, we propose a lightweight oriented spatial convolution method to capture directional defect features. Finally, to enhance information interaction between features at different scales, we introduce the CGAFusion feature fusion mechanism to effectively integrate high-frequency texture information with low-frequency semantic information. The experimental results show that the network proposed in this paper exhibits excellent performance on PKU-Market-PCB and DeepPCB datasets. Compared with the baseline model, mAP50 and mAP50–95 improved by 2.9% and 1.2% respectively, reduced computational complexity by 11.1%, and reduced parameter count by 26.6%. The overall performance is better than popular object detection networks. The results validated the detection performance of MPHF-Net, providing a new approach for industrial quality inspection.