In order to solve the problem of low efficiency of traditional manual inspection, artificial intelligence and computer vision technology was combined to propose a pixel-level refined automatic identification and segmentation method for concrete dam cracks under complex backgrounds.To overcome the interference of complex environmental background factors, the concrete dam crack image is preprocessed based on various digital image processing methods to effectively remove environmental noise. Based on the Mask R-CNN instance segmentation model, the model backbone network is optimized to improve the crack feature extraction ability. In the experiment, 500 images of concrete dam cracks, including single crack, multiple cracks, cross cracks, map cracks and other crack forms, were collected for model training and verification. A combination of qualitative analysis and quantitative evaluation was used to evaluate the generalization ability and robustness of the model in multiple dimensions. The test results show that the improved Mask R-CNN model in this paper has a good effect on crack image recognition with various crack features and noise backgrounds. The average accuracy values of the model’s target detection and mask segmentation on the test set are 76.3 and 61.9, respectively, which can meet the requirements of high-precision fine segmentationfor cracks. Compared with benchmark models such as Cascade-Mask R-CNN and Yolact++, the improved Mask R-CNN method has certain advantages in object detection, mask segmentation accuracy and model inference speed.