Research on computer vision in intelligent damage monitoring of heritage conservation: the case of Yungang Cave Paintings

To enhance the precision of damage monitoring of cave paintings in cultural heritage protection and to realize fast real-time detection, this paper proposes an intelligent monitoring algorithm using computer vision technology, called YOLO CP(Cave Paintings). The C2f-FasterEMA Block module is developed by the algorithm, which also refines the deep layer’s residual module in the backbone network. This improvement boosts the capability to extract target features while minimizing the number of parameters. Moreover, the RepGD (Rep Gather-and-Distribute) mechanism has been integrated into the feature fusion network, thereby boosting the capability of cross-layer feature information fusion and enhancing the model’s detection accuracy. Ultimately, Inner-SIoU (Linear Spatial Intersection over Union) is presented to enhance the loss function. This approach utilizes a more appropriate aspect ratio metric and addresses the shortcomings of the original loss function, thereby speeding up the convergence of the model. The findings from the experiment indicate that the YOLO CP algorithm decreases the parameters and floating-point operations by 8.14% and 7.14%, respectively, when compared with the original YOLOv10n baseline algorithm in the task of detecting damage in cave paintings. At the same time, the YOLO CP model shows enhancements in Precision and Recall by 1.82 and 7.05 percentage points, respectively. The speed of detection in real-time achieves 277.39 FPS. The proposed algorithm demonstrates a considerable enhancement in performance, offering both theoretical and technical backing for the automated and intelligent detection of cave painting damage. Additionally, it holds the promise of quick implementation on embedded devices, serving as a crucial technical assurance for the preservation of cultural artifacts and heritage.