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 largearea 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 cm 2 , and the model achieves 91. 2% MIoU through training, which enables the detection of defects on complex surfaces and planes of multiple materials.