Applications of fiber optic sensors in traffic monitoring: a review

Abstract

Fiber optic sensors are becoming key tools in building more robust and accurate traffic monitoring systems. In this survey, we present various preliminary implementations and a discussion about some of the key design points. The scanning rates of the sensors should be at least 500 Hz to correctly capture the moving vehicles. The size of the sensors should not be larger than approximately 50 cm to enable individual axle detection. A minimum of 1µ sensor resolution is required to detect light weight vehicles. Adaptive filtering, Wavelet denoising and neural networks are some of the tools used to improve the performance. Sensors that reside on the surface of the pavement might lead to biased data and cause destructive implementation.There is yet to be a unified solution. We need a global, user-friendly, consistent, durable, environment-friendly solution with high system availability that would work on bridges, in urban areas and in tunnels. We believe precast slabs of road pavement embedded with sensors that are manufactured at a facility would give us the best opportunity to meet those demands.

Invisible track bed defect classification method based on distributed optical fiber sensing system and FFT Attention Transformer model

“…In recent years, with the rapid development of optical fiber sensing technology, distributed optical fiber sensing technology has been widely used in the safety and health detection of various structures because of its advantages of high precision, high reliability, anti-interference, long distance transmission, and long detection distance 9 – 17 The distributed sensing detection system of a multiplexing ultra-weak fiber Bragg grating (UWFBG) has attracted the attention of researchers and has been applied to the actual safety inspection of metro corporation due to its advantages of high sensitivity, fast response, and dynamic measurement 18 , 19 . At present, the application of a sensor system in subway safety detection is mainly in tracking the speed and positioning the subway trains, 20 detecting personnel intrusions, 21 detecting external construction disturbances, and 22 classifying abnormal events 23 .…”