Abstract: [Purposes] To address missed detections caused by the small size，weak feature representation，and complex-background interference of safety helmets，reflective vests，and other personal protective equipment in railway UAV imagery，this paper proposes a detection method integrating human spatial-structure priors and shallow feature enhancement. [Methods] Based on YOLO11n，prior regions are constructed according to the spatial relationships between personnel，safety helmets，and reflective vests.A region-weighted classification loss is designed to strengthen the learning of protective equipment within anatomically reasonable regions.Meanwhile，a high-resolution shallow feature branch is introduced to improve small-object representation. [Findings] On the self-built dataset，the proposed method achieved a P of 92.3%，R of 89.2%，mAP _0.5 of 94.3%，and mAP _0.5：0.95 of 50.7%，outperforming the baseline by 8.6，9.1，8.7，and 9.0 percentage points，respectively. [Conclusions] The proposed method effectively improves the detection accuracy of personnel and protective equipment in railway UAV imagery，providing technical support for construction safety localization and railway safety management．

Key words: UAV image, UAV inspection, personal protective equipment detection, human spatial-structure prior, small object detection