Accurate　bow　direction　detection　is　essential　for　ship　trajectory　prediction　and　port　monitoring.　Existing　ship　detection　networks　typically　output　angles　within　180　degrees,　while　extending　to　360　degrees　introduces　cyclic　issues　affecting　rotation　intersection　over　union　(RIoU)　accuracy.　This　study　proposes　a　novel　bow　direction　detection　algorithm　that　extends　network　output　to　360　degrees　and　integrates　a　heading　intersection　over　union　(HIoU)　loss　to　enhance　detection　accuracy　and　robustness.　Additionally,　an　HIoU　loss　function　is　designed　to　improve　bow　direction　identification　and　reduce　quantization　errors　in　hash　codes.　The　algorithm　is　evaluated　on　three　datasets:　FGSD,　OHD-SJTU-S,　and　OHD-SJTU-L.　On　FGSD,　it　achieves　mean　average　precision　(mAP)　of　91.14%.　On　OHD-SJTU-S,　it　attains　an　mAP50:95　of　63.3%　and　a　bow　direction　prediction　accuracy　of　90.7%.　On　OHD-SJTU-L,　the　mAP50:95　is　29.2%,　with　an　accuracy　of　80.2%.