The third-party interference, such as construction activities and man-made sabotage, has become the leading cause of pipeline accidents in the recent years. This work is devoted to a real-time surveillance system for safety monitoring and early warning of buried municipal pipelines subject to the most common abrupt intrusions based on distributed fiber optic sensors using the phase-sensitive optical time-domain reflectometry(φ-OTDR). A two-layer classifier based on convolutional neural network (CNN) is developed: one layer is used to discriminate the third-party threats from pedestrian and traffic noises; the other layer is to determine the specific type of third-party interference. To reduce false alarm times, the time-space matrixes are built to correct the possible errors. Field tests on an optical fiber cable buried in roads and residential areas are carried out to validate the two-stage surveillance system. It shows the first layer can effectively solve the problem of false alarm, while the second can accurately recongize the specific type of third-party interference.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.