Traffic jam area real-time detection method based on deep learning

A technology of traffic congestion and deep learning, applied in the field of deep learning, can solve the problems of slow detection speed and low detection accuracy

Active Publication Date: 2019-09-27
ZHEJIANG UNIV OF TECH
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

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 b

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  • Traffic jam area real-time detection method based on deep learning
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  • Traffic jam area real-time detection method based on deep learning

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Embodiment Construction

[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 traffic jam area real-time detection method based on deep learning. The traffic jam area real-time detection method comprises the following steps: 1, setting a camera preset position and calibrating a camera; 2, initializing a convolutional neural network model; 3, acquiring a current video frame and video frame time; 4, checking the working state of the camera; 5, performing vehicle target detection on the ROI by using a convolutional neural network model; 6, maintaining a stationary target tracking queue; 7, detecting a congestion area; and 8, congestion status reporting. The congestion detection area congestion judgment algorithm provided by the invention has relatively strong robustness to environmental changes, and realizes a real-time detection effect and relatively high congestion identification precision.

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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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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