In　the　process　of　defect　detection　for　various　materials　with　complex　curved　surfaces　or　flat　surfaces,　conventional　tactile　sensors　have　disadvantages,　such　as　small　detection　area　and　low　detection　efficiency.　To　address　these　problems,　a　fingertip　type　large-area　optical　tactile　sensor　is　designed　and　prepared　for　multi-material　surface　defect　detection.　It　is　similar　to　the　tip　of　human　finger　and　has　both　finger-shaped　curved　and　flat　contact　surfaces,　which　can　meet　the　detection　needs　of　various　complex　contact　surfaces.　A　miniature　actuator　is　designed　in　the　sensor　to　drive　the　camera　rotation　to　improve　the　imaging　quality,　and　multiple　images　are　collected　by　rotation　and　stitched　together　using　the　APAP　image　stitching　algorithm　to　increase　the　effective　area　for　single　detection.　A　variety　of　material　surface　defects　are　simulated　and　a　tactile　image　dataset　is　created,　which　is　trained　by　the　DeepLabv3　model.　Experimental　results　show　that,　with　a　single　acquisition,　the　effective　detection　area　reaches　16.　3　cm2,　and　the　model　achieves　91.　2%　MIoU　through　training,　which　enables　the　detection　of　defects　on　complex　surfaces　and　planes　of　multiple　materials.　©　2023　Science　Press.　All　rights　reserved.