Visual-tactile　sensors　that　use　a　camera　to　capture　the　deformation　of　a　soft　gel　layer　have　become　popular　in　recent　years.　However,　these　sensors　have　a　limited　receptive　field,　which　can　hinder　their　ability　to　perceive　tactile　information　effectively.　In　this　letter,　we　propose　a　novel　visual-tactile　sensor　named　GelFinger　that　closely　resembles　the　human　finger　and　is　well-suited　for　detecting　various　complex　surfaces.　The　GelFinger　sensor　is　equipped　with　an　embedded　miniature　motor　that　allows　for　the　adaptation　of　the　camera　pose　and　the　scanning　of　a　large　contact　area.　During　the　detection　process,　the　camera　rotates　to　multiple　angles　to　capture　the　tactile　image　of　the　contact　area.　To　stitch　together　the　tactile　images　obtained　at　different　camera　poses,　we　use　an　As-Projective-As-Possible　image　stitching　algorithm　to　form　a　global　view　of　the　contact.　We　demonstrate　the　effectiveness　of　the　GelFinger　sensor　in　assessing　large　surfaces　by　using　it　to　reconstruct　curved　crack　outlines.　Comparative　experimental　results　show　that　the　proposed　sensor　can　effectively　detect　cracks　and　has　the　potential　to　assist　humans　in　detecting　defects　on　curved　surfaces　of　infrastructure　such　as　pipelines.