To address the challenges of detecting small-object defects on Printed Circuit Board （PCB） surfaces， such as low contrast， irregular shapes， and susceptibility to background interference， a dual-branch feature detection model named Dual branch feature Extraction-Spatial attention Detection （DES-Det） was proposed. First， a dual-branch collaboration was used to extract global contextual and local detailed features， thereby enhancing the discriminative capability for low-pixel defects. Second， a deformable attention mechanism was introduced to achieve adaptive feature focusing， thereby improving the localization accuracy for blurred edges and irregular defects. At the same time， a detection head based on spatial attention and depthwise separable convolution was designed to reduce model complexity significantly while maintaining detection performance. Experimental results on two public PCB defect datasets demonstrate that DES-Det achieves precisions of 93.6% and 98.4%， respectively， representing improvements of 5.7 and 1.4 percentage points， respectively， compared to the baseline model YOLOv8， with the number of parameters reduced by 26.7%. Cross-scenario generalization experimental results further validate the method's effectiveness and robustness in complex industrial environments.