Optical fiber perimeter intrusion monitoring method based on image recognition

Abstract

The invention relates to an optical fiber perimeter intrusion monitoring method based on image recognition. The monitoring method is completed by an optical fiber vibration sensing system. The system comprises detection cables which are connected together, a monitoring host and an upper computer. The monitoring method comprises the steps that vibration caused by outside intrusion is detected by the detection cables, and an optical signal is transmitted to the monitoring host; the monitoring host converts the received optical signal into an electrical signal, and then carries out sampling and analog-digital conversion on the electrical signal to acquire a discrete digital signal; the digital signal is transmitted to the upper computer; the upper computer processes the acquired digital signal to acquire processed signal feature amount to form one or more waterfall curves; according to the form of the waterfall curve, image pattern recognition is carried out; and an intrusion event is determined, intrusion alarm is triggered. According to the optical fiber perimeter intrusion monitoring method provided by the invention, additional equipment is not needed; real-time intrusion recognition is realized without a storm database; and the method has the advantages of high intrusion recognition rate and low false alarm rate.

Description

technical field [0001] The invention relates to intelligent monitoring, in particular to an optical fiber perimeter intrusion monitoring method based on image recognition technology. Background technique [0002] With the construction of smart cities, the current perimeter security is becoming more and more important. The perimeter security monitoring system based on distributed optical fiber sensing is showing superior performance over traditional perimeter security. The distributed fiber optic perimeter security system will also be disturbed by wind and rain, heavy trucks, trains, etc., resulting in certain false alarms. In order to better improve the performance, it should not only be based on the existing signal demodulation technology, but also make full use of the growing pattern recognition technology. [0003] In the actual station environment, wind, rain, hail, tree branch touch, animal touch, bird start and stop, non-alarm signals, and vibrations caused by passin...

AI Technical Summary

Problems solved by technology

[0003] In the actual station environment, wind, rain, hail, tree branch touch, animal touch, bird start and stop, non-alarm signals, and vibrations caused by passing vehicles will generate disturbance signals similar to the intrusion intensity level, resulting in false alarms. report, but the shape of these disturbances in the waterfall chart will be different, such as figure 1 As shown, A in the figure is the wind blowing behavior, B is the 3 times knocking behavior

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