Underwater　pile-pier　structures　are　important　components　of　bridges.　Various　surface　defects　occur　on　these　structures　due　to　their　complex　hydrological　environment.　Existing　methods　for　the　visual　detection　of　such　defects　have　two　main　issues:　(1)　Underwater　images　are　blurred,　and　the　colors　are　severely　distorted;　(2)　the　size　of　defects　cannot　be　quantitatively　identified,　and　the　detection　efficiency　is　low.　To　solve　these　problems,　this　paper　proposes　a　method　to　extract　the　contours　of　underwater　pile-pier　surface　defects　by　combining　an　image　fusion　enhancement　algorithm　with　a　deep　learning　model.　First,　a　pixel-level　image　fusion　algorithm　based　on　point　sharpness　weights　is　used,　which　can　fuse　two　single　enhanced　images　as　well　as　significantly　improve　image　contrast　while　ensuring　effective　color　correction.　Second,　the　DeepLabv3+　semantic　segmentation　network　model　is　improved　in　terms　of　weight,　such　that　the　number　of　weight　parameters　required　for　the　model　can　be　reduced　as　much　as　possible　while　maintaining　the　accuracy.　Next,　an　open-source　dataset　of　surface　defects　in　building　structures　is　used　to　train　the　backbone　feature　extraction　network　layer,　and　the　transfer　learning　method　is　applied　to　the　detection　task　of　the　object　domain.　Finally,　the　image　dataset　collected　from　underwater　experiments　and　practical　engineering　works　is　used　to　train　the　light-weight　improved　model,　establish　the　underwater　pile-pier　surface　defect　contour　extraction　model,　and　then　verify　and　test　the　models.　In　addition,　comparisons　focusing　on　three　aspects,　namely,　comparison　with　five　other　commonly　used　algorithms,　comparison　of　detection　results　with　and　without　image　fusion,　and　comparison　with　and　without　noise　effects,　are　made　to　verify　the　robustness　and　effectiveness　of　the　proposed　method.　The　results　show　that　the　image　fusion　enhancement　algorithm　proposed　in　this　paper　can　effectively　enhance　the　detailed　features　of　the　defect　image　contours,　and　the　light-weight　improved　model　has　the　highest　recognition　accuracy　and　can　maintain　high　detection　efficiency　and　robustness.　This　implies　that　the　proposed　method　is　suitable　for　the　quantitative　detection　of　the　surface　defect　contours　of　underwater　pile-pier　structures　implanted　in　small　underwater　robots　for　practical　bridge　structures.　©　2024　Chang＇an　University.　All　rights　reserved.