Ship　target　detection　on　the　sea　surface　is　one　of　the　most　common　scenes　in　the　field　of　target　detection.　The　accuracy　of　target　detection　plays　an　important　role　in　the　field　of　sea　rescue　and　defense.　However,　complex　infrared　sea　surface　scenes,　such　as　island　shore,　fish　scale　wave,　sea　surface　bright　band　and　other　disturbances,　bring　great　challenges　to　target　detection.　In　this　paper,　we　propose　an　improved　YOLOV5　model　by　analyzing　the　characteristics　of　infrared　image　imaging　and　ship　target　on　the　sea　surface.　Aiming　at　the　problem　of　information　loss　of　small　targets　in　the　deep　layer　of　the　network,　we　redesigned　the　backbone　network,　which　was　composed　of　four　Multi-scale　residual　blocks,　and　each　block　was　connected　by　CBAM　(Convolutional　Block　Attention　Module)　attention　mechanism　to　improve　information　transmission　and　fusion　between　different　feature　layers.　Aiming　at　the　problem　of　complex　sea　surface　scene　interference,　we　use　FPN　and　PAN　(Feature　Pyramid　Network　and　Personal　Area　Network)　mechanism　to　construct　feature　fusion　network　in　neck,　and　add　shallow　feature　fusion　in　FPN.　Our　model　achieves　good　performance　both　in　terms　of　latency　and　accuracy.　In　a　self-collected　sea　surface　scene　dataset,　multiple　SOTAs　for　detection　tasks　are　compared　and　the　results　demonstrate　the　superiority　of　the　proposed　method.　©　2022　ACM.