Perimeter monitoring of urban buried pipeline threated by construction activities based on distributed fiber optic sensing and real-time object detection.

ABSTRACT

Urban construction activities seriously jeopardize the security of buried pipeline. Distributed optical fiber vibration monitoring is one of the most promising ways to prevent third-party threats, of which the biggest challenge is to quickly and accurately detect rare abnormal events from extremely large amounts of time-space raw data. By analogy with image recognition, the task here is similar to object detection if considering the time-space optical signals as the grayscale images and the abnormal events as the objects. Given this, what we believe to be a novel monitoring method is proposed, which consists of two Faster R-CNN models, a max pooling layer and a monitoring strategy. In the field tests, the 86-hour optical vibration signals for 5.25 km distance are recognized within 6.6 minutes with the recognition rate of 98.85% for construction activities, and only two false alarms are issued. The proposed method can reduce the recognition time by 99.59% compared to the CNN-based method.