A Low-Cost Image-Based Monitoring System for Automatic Rock Displacement Measurement Using YOLO

Accurate monitoring of rock movement is essential for understanding rock slope instability and early failure mechanisms. However, conventional monitoring techniques often rely on manual measurements or expensive instrumentation, limiting their practicality for continuous and large-scale deployment. This study presents a low-cost image-based monitoring system that enables fully automatic measurement of rock displacement using a YOLOv8 pose estimation model. The proposed system was evaluated at the Miroir d’Argentine rock wall, a limestone rock flake in the Swiss Alps, Switzerland, using image data acquired from a Futuro BRW comparator monitored by a low-cost digital camera. The model was trained to detect the manual dial indicator and estimate pointer positions, allowing rock displacement to be derived automatically from pictures without manual intervention. To further assess the practical applicability of the proposed system, additional images acquired at different time periods were used for independent validation. The results demonstrate that the proposed approach can reliably estimate rock movement with high accuracy and strong generalization capability under real-world conditions. In addition, the robustness of the method was evaluated under simulated fog and blur degradations. The results show that the system maintains stable performance under light to moderate visual degradation, while performance decreases under severe fog and strong motion blur due to reduced geometric visibility. Overall, the proposed method provides an effective, low-cost, and practical solution for continuous rock movement monitoring and shows strong potential for long-term deployment in challenging alpine environments.