Urban-level traffic video detection equipment quality detection method

Abstract

The invention discloses an urban-level traffic video detection equipment quality detection method. The method comprises the following steps of a, constructing the network topology structure of each video detection device in a road network, and acquiring a network topology structure table; b, according to the network topology structure table, constructing the randomness model of road section strokeconfidence time; c, identifying correct vehicle passing data and error data in a total equipment detection amount N detected by the video detection device S and calculating the false detection rate of the video detection device S; and traversing all the video detection devices and acquiring the false detection rates of all the video detection devices; and d, according to the road section stroke confidence time, randomly simulating missing data, defining the stability index of a road section vehicle passing amount, and calculating the omission ratios of the video detection devices in a networktopology structure table. The method has characteristics that traffic data quality is improved; a workload is reduced; and data description accuracy is increased. And the method is the basis of intelligent traffic platform construction.

Description

technical field [0001] The invention relates to the technical field of data quality monitoring, in particular to a quality detection method for city-level traffic video detection equipment. Background technique [0002] Most cities in China have experienced several years of intelligent transportation construction, installed a large number of traffic data collection equipment, and collected massive traffic data. However, when the traffic management department wants to use big data technology to alleviate urban traffic congestion, it is very difficult. The main reason is that during the equipment assembly process, due to the lens quality, assembly process and other reasons, the accuracy rate will drop slightly. Interferenced by factors such as light, equipment installation angle, traffic density, license plate suspension method, etc., the collected data inevitably has noise. Data sets with a large number of errors cannot support the effective use of smart transportation syste...

AI Technical Summary

Problems solved by technology

However, when the traffic management department wants to use big data technology to alleviate urban traffic congestion, it is very difficult. The main reason is that during the equipment assembly process, due to the lens quality, assembly process and other reasons, the accuracy rate will drop slightly. Interferenced by factors such as light, equipment installation angle, traffic density, license plate suspension method, etc., the collected data inevitably has noise

Datasets with a large number of errors cannot support the effective use of intelligent transportation systems

And at present, it only monitors the power failure of the equipment, and has not tested the indicators such as the error rate and missed detection rate of the equipment.

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