Road congestion condition detection method taking robustness vehicle target detection as core

Abstract

The invention discloses a road congestion condition detection method taking robustness vehicle target detection as a core. The method comprises the following steps: firstly, carrying out vehicle detection by adopting a YOLOv3 model network; secondly, aiming at the problem of repeated counting caused by jitter of the central point of a vehicle detection frame, realizing accurate counting of vehicles by judging whether the coordinates of the central point of the vehicle are located in the vehicle detection frame or not and judging whether the intersection of the vehicle detection frame and the vehicle detector is greater than a set threshold value or not, and recording the position and size information of the vehicles in a certain time; performing information screening by using NMS to estimate the maximum bearing capacity; and finally, specifically quantifying the congestion index CI, wherein the congestion degree of the road section can be accurately judged by adopting the congestion index. The method can directly, clearly, simply, conveniently and economically obtain the road traffic jam condition in real time, is convenient for local traffic police departments to apply, and has decision reference value.

Description

technical field [0001] The invention relates to a traffic jam detection method, in particular to a road congestion detection method with robust vehicle target detection as the core. Background technique [0002] With the gradual improvement of people's living standards, the per capita occupancy rate of motor vehicles has increased significantly, and urban traffic congestion has become increasingly serious. Traffic congestion is a phenomenon in which traffic activities are slow and interrupted due to traffic volume exceeding road capacity, resulting in travel delays and environmental pollution causing massive economic losses. Therefore, it is of great significance to study traffic congestion detection methods to make targeted preventive measures for traffic congestion. For traffic congestion detection, the traditional method uses sensors to obtain traffic flow parameters such as the number of vehicles on the road and vehicle speed, which has the following disadvantages: [...

AI Technical Summary

Problems solved by technology

[0022] In view of the disadvantages of traditional urban traffic road congestion detection methods, such as high maintenance cost, damage to the road surface, insufficient accuracy, and poor real-time performance

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