A real-time detection method for traffic jam areas based on deep learning

A traffic congestion and deep learning technology, applied in the field of deep learning, can solve the problems of low detection accuracy and slow detection speed, and achieve the effect of ensuring traffic safety, alleviating traffic congestion, and real-time detection effect.

Active Publication Date: 2021-08-03
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0005] In order to overcome the shortcomings of low detection accuracy and slow detection speed in the prior art, the present invention provides a real-time detection method for traffic congestion areas based on deep learning, using deep convolutional neural network (CNN) features for vehicle target detection, and integrating video spatiotemporal Continuity for accurate and fast congestion area detection

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  • A real-time detection method for traffic jam areas based on deep learning
  • A real-time detection method for traffic jam areas based on deep learning
  • A real-time detection method for traffic jam areas based on deep learning

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[0057] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, and do not limit the protection scope of the present invention.

[0058] Such as Figure 1-3 As shown, the deep learning-based real-time detection method for traffic jam areas provided in this embodiment includes the following steps:

[0059] S1, camera preset position setting and camera calibration.

[0060] Specifically, adjust the camera to the appropriate detection position of the traffic congestion area, and set the current camera position as the preset position; then intercept a frame image of the camera video stream, and perform lane lines, ROI, and multiple congestions on it. The calibration of the detection area, and the con...

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Abstract

The invention discloses a method for real-time detection of traffic jam areas based on deep learning, which includes the following steps: 1) camera preset position setting and camera calibration; 2) convolutional neural network model initialization; 3) obtaining the current video frame and video Frame time; 4) Check the working status of the camera; 5) Use the convolutional neural network model to detect vehicle targets in ROI; 6) Maintain the static target tracking queue; 7) Congestion area detection; 8) Congestion status reporting. The invention proposes a congestion judgment algorithm for a congestion detection area, which has strong robustness to environmental changes, realizes real-time detection effect and high congestion recognition accuracy.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to a method for real-time detection of traffic jam areas based on deep learning. Background technique [0002] In recent years, with the popularization of automobiles, the capacity of urban roads has been insufficient, and with improper design and too many road intersections, the problem of traffic congestion has become extremely prominent. Traffic congestion will not only affect people's travel efficiency, but also Even more can cause serious traffic accident, seriously endanger people's travel safety. Therefore, it is particularly important to accurately detect and report traffic congestion in real time. [0003] At this stage, there are mainly three solutions proposed by researchers for the traffic congestion problem. The first one is to predict congestion based on the statistical characteristics of road traffic flow, such as historical traffic volume, headway, average spe...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/32G06N3/04G06T7/80G08G1/01
CPCG06T7/80G08G1/0133G06V20/41G06V10/25G06N3/045
Inventor 高飞王金超葛一粟李帅卢书芳张元鸣邵奇可陆佳炜
Owner ZHEJIANG UNIV OF TECH
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