DEVELOPMENT OF A TUNNEL FACE ROCK FALLS MONITORING SYSTEM WITH IMAGE PROCESSING

Abstract

 During the construction work of mountain tunnels, monitoring the tunnel face is required all the time to prevent labor accidents. Small rock falls can be a sign of a large scale collapse, despite the time lag between them. However, it is difficult for the supervisor to detect small rock falls by visual monitoring. Therefore, in this research, a monitoring system was developed for detecting falling objects based on the background substraction. In order to monitor small rock falls, we limited the analysis scope of detecting falling objects process to the tunnel face which was detected by semantic segmentation and removed minute objects such as tunnel dust and video noise. Next, we conducted an image analysis by the developed system using footages which simulate falling rocks, the sign of a large scale collapse. The result showed that small rock falls could be detected, which implies the mitigation of labor accidents.