To address the missed detection of small prohibited items in X-ray security inspection images due to low pixel ratios and ambiguous features, this study proposes a detection algorithm based on fine-grained feature enhancement. First, we design a learnable spatial reorganization module that replaces traditional downsampling operations with dynamic pixel allocation strategies to reduce fine-grained feature loss. Second, we construct a dynamic basis vector multi-scale attention module that adaptively adjusts the number of basis vectors according to feature entropy, enabling cross-dimensional feature interaction. Finally, we introduce a 160×160 high-resolution detection head that reduces the minimum detectable target size from 8 pixel×8 pixel to 4 pixel×4 pixel. Experimental results demonstrate that on the SIXray, OPIXray, and PIDray datasets, our algorithm achieves mean Average Precision (mAP) values of 93.3%, 91.2%, and 86.9%, respectively, showing improvements of 1.2%?3.1% over the YOLOv8 baseline model while only increasing the parameter count by 0.2×10⁶.