Aiming　at　the　environments　such　as　ports　with　dense　ship　traffic　and　sea　voyages　with　complex　environments,　this　paper　proposes　an　efficient　YOLOv8　ship　detection　method　based　on　lightweight　improvement.　Firstly,　the　CA　attention　module　is　added　to　the　original　network　structure　of　YOLOv8,　which　helps　the　model　to　perform　feature　extraction　and　target　localization　more　accurately;　secondly,　the　DualConv　module　is　introduced　to　establish　a　lightweight　network,　which　optimizes　the　process　of　information　processing　and　reduces　the　amount　of　floating-point　computation　efficiently;　the　experimental　results　show　that　the　lightweight　algorithm　achieves　an　accuracy　of　98.7%,　and　at　the　same　time　the　algorithm　has　a　GFLOPs　of　7.5,　which　is　8.5%　lower　than　the　original　model,　and　the　Parameters　of　the　model　is　2.65M,　which　is　12%　lower　than　the　original　model.　It　can　better　meet　the　demand　of　rapid　ship　detection　in　the　harbor　site　and　provides　an　effective　solution　for　real-time　monitoring　in　the　maritime　field.　©　2024　IEEE.