Abstract: Occlusion of varying degrees and the challenges associated with modeling complex occlusion relationships are critical factors affecting pedestrian detection performance. To address these issues, an enhanced occlusion aware pedestrian detection method is proposed based on the end-to-end detection framework. First, an occlusion prior positional encoder is introduced to model occlusion relationships and provide occlusion prior information to the multi-layer decoder. Second, a multi-scale feature fusion heatmap predictor is designed to accurately capture the target center position and enhance target perception ability, thereby improving detection robustness in occluded scenes. Finally, comparative and ablation experiments are conducted on the CrowdHuman and CityPersons datasets. The experimental results demonstrate that the proposed method effectively alleviates the issues of missed detections and false positives under occlusion conditions.