Damage detection and digital reconstruction method for grotto murals based on YOLOv10

In this paper, a deep learning-based method is proposed for the fast and high-precision detection and digital reconstruction of damaged areas in grotto murals. First, the YOLO Mural breakage detection algorithm is presented, which is based on the YOLOv10 algorithm. The algorithm enhances the backbone network structure through the incorporation of a Star Block module and the parameter-free attention mechanism, SimAM. This modification aims to improve the capture of subtle mural damage features, thereby enhancing the detection precision. In addition, an Efficient-RepGFPN feature fusion network is implemented. The fusion of low-level features with high-level semantic information enabled the model to extract richer feature information. Additionally, the loss function is optimized through the adoption of Inner-SIoU, which allows the model to focus more on locating damaged areas to enhance precision. The AOT-GAN generative adversarial network is utilized to digitally reconstruct the damaged sections of the grotto murals. The experimental results show that the YOLO Mural model has a detection precision of 72.7%, a recall rate of 60.1%, [email protected] of 63.7%, a harmonic mean of 66%, and a real-time detection speed of 289.32 frames per second. Compared to YOLOv10, the YOLO Mural model exhibited a precision increase of 4.15 percentage points and a recall increase of 10.68 percentage points. In comparison with RT-DETR, YOLO v9, YOLO v8, and YOLO v7 models, the YOLO Mural model has the fewest number of parameters, only 2.65 million. When applied to grotto mural restoration, the AOT-GAN model exhibited a PSNR and SSIM of 34.842 and 0.9832, respectively, providing an important reference for subsequent restoration work and potentially saving a significant amount of labor costs.