Underwater　target　detection　plays　an　important　role　in　ocean　exploration,　to　which　the　improvement　of　relevant　technology　is　of　much　practical　significance.　Although　existing　target　detection　algorithms　have　achieved　excellent　performance　on　land,　they　often　fail　to　achieve　satisfactory　outcome　of　detection　when　in　the　underwater　environment.　In　this　paper,　one　of　the　most　advanced　target　detection　algorithms,　YOLOv5　(You　Only　Look　Once),　was　first　applied　in　the　underwater　environment　before　being　improved　by　combining　it　with　some　methods　characteristic　of　the　underwater　environment.　To　be　specific,　the　Swin　Transformer　was　treated　as　the　basic　backbone　network　of　YOLOv5,　which　makes　the　network　suitable　for　those　underwater　images　with　blurred　targets.　It　is　possible　for　the　network　to　focus　on　fusing　the　relatively　important　resolution　features　by　improving　the　method　of　path　aggregation　network　(PANet)　for　multi-scale　feature　fusion.　The　confidence　loss　function　was　improved　on　the　basis　of　different　detection　layers,　with　the　network　biased　to　learn　high-quality　positive　anchor　boxes　and　make　the　network　more　capable　of　detecting　the　target.　As　suggested　by　the　experimental　results,　the　improved　network　model　is　effective　in　detecting　underwater　targets,　with　the　mean　average　precision　(mAP)　reaching　87.2%,　which　makes　it　advantageous　over　general　target　detection　models　and　fit　for　use　in　the　complex　underwater　environment.