With　the　advancements　in　deep　learning,　detecting　occluded　pedestrians　has　become　a　focal　point　of　research.　Extracting　pedestrian　parts　has　proven　to　be　an　effective　solution　for　handling　occlusion.　However,　existing　methods　mainly　rely　on　Region　Proposal　Networks　(RPNs)　for　the　part　feature　extraction.　These　RPNs-based　methods　suffer　from　limitations　such　as　complex　structures　and　limited　receptive　fields,　which　hinder　their　ability　to　capture　global　dependency　information.　To　overcome　these　challenges,　we　propose　a　simple　but　effective　Occlusion-Aware　Dual-Branch　Network　(OADB-Net)　based　on　an　anchor-free　framework　for　pedestrian　detection　in　crowded　scenes.　Specifically,　we　design　a　dual-branch　occlusion-aware　detection　head,　consisting　of　a　full-body　detection　branch　and　a　head-shoulder　detection　branch,　to　address　the　occlusion　issue　in　crowded　scenes.　The　head-shoulder　detection　branch　to　handle　heavily　occluded　instances　and　the　full-body　branch　to　focus　on　non-heavily　occluded　instances.　Furthermore,　we　propose　a　Cross-Layer　Non-Local　Module　(CLNL-Module),　which　captures　long-range　dependencies　across　feature　layers,　to　effectively　differentiate　the　relationship　between　pedestrian　body　and　body　parts　while　integrating　essential　features　for　more　accurate　and　discriminative　pedestrian　representation.　This　strengthens　the　connections　between　the　dual　detection　branches　and　enhances　the　responses　of　their　respective　center　heatmaps.　Our　OADB-Net　leverages　part　and　full-body　features　to　handle　pedestrians　with　varying　degrees　of　occlusion,　while　avoiding　the　limitations　of　RPNs-based　methods.　In　heavy　occlusion　settings,　OADB-Net　achieves　the　average　miss　rates　of　39.9%,　26.8%,　and　43.1%　on　the　Citypersons,　Caltech,　and　CrowdHuman　datasets,　respectively,　and　demonstrates　superior　performance　in　traffic　scenes.