A road congestion detection method based on robust vehicle target detection

A technology of vehicle detection and detection method, which is applied in the direction of road vehicle traffic control system, traffic flow detection, instrument, etc., can solve the problems of insufficient accuracy, high maintenance cost, damage to the road surface, etc., to improve instability and robustness Good, high detection accuracy

Active Publication Date: 2022-05-06
东土科技(宜昌)有限公司
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Problems solved by technology

[0022]Aiming at the shortcomings of traditional urban traffic road congestion detection methods, such as high maintenance cost, damaged road surface, insufficient accuracy, and poor real-time performance

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  • A road congestion detection method based on robust vehicle target detection
  • A road congestion detection method based on robust vehicle target detection
  • A road congestion detection method based on robust vehicle target detection

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specific Embodiment

[0093] The detection of urban traffic road congestion needs to be real-time and reliable at the same time. The traditional methods of judging congestion have the disadvantages of obtaining inaccurate road surface information and relying too much on historical data. In order to obtain accurate road vehicle information in real time and provide accurate road vehicle information for congestion detection, the vehicle must first be detected in real time and accurately. YOLOv3 is an end-to-end target detection algorithm with both speed and accuracy. It uses a large number of residual structures in the feature extraction network to ensure that the deep network can effectively extract the features of the target. Targets of different sizes are positioned, and the output network is divided into three layers, corresponding to three different sizes of targets: large, medium and small. At the same time, since a network with a fixed output size will limit the receptive field of the output ne...

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Abstract

A road congestion detection method with robust vehicle target detection as the core. Firstly, the YOLOv3 model network is used for vehicle detection; Accurate counting of vehicles is achieved by checking whether the intersection of the frame and the vehicle detection frame and the vehicle detector is greater than the set threshold, and record the position and size information of the vehicle within a certain period of time; use NMS to filter information and estimate the maximum carrying capacity; finally, the specific Quantify the congestion index CI, and use the congestion index to accurately judge the congestion level of the road section. The method of the invention can directly and clearly obtain the road traffic congestion situation in a simple, convenient, economical and real-time manner, is convenient for local traffic police departments to apply, and has decision-making reference value.

Description

technical field [0001] The invention relates to a traffic congestion 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 the phenomenon of 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 foll...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/01G08G1/065
CPCG08G1/0133G08G1/065Y02T10/40
Inventor 徐光柱刘高飞万秋波储志杰钱亦凡雷帮军
Owner 东土科技(宜昌)有限公司
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