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A real-time detection method for abnormal parking based on deep learning

A technology of abnormal parking and real-time detection, applied in the field of deep learning, can solve problems such as low detection accuracy and slow detection speed, and achieve the effects of ensuring traffic safety, improving traffic efficiency, and strong robustness

Active Publication Date: 2021-07-20
ZHEJIANG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0004] 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 abnormal parking based on deep learning, which uses deep convolutional neural network (CNN) features to detect vehicle targets, and combines video space-time continuous Accurate and fast abnormal parking detection

Method used

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  • A real-time detection method for abnormal parking based on deep learning
  • A real-time detection method for abnormal parking based on deep learning
  • A real-time detection method for abnormal parking based on deep learning

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Embodiment Construction

[0067] 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.

[0068] like Figure 1-3 As shown, the deep learning-based real-time detection method for abnormal parking provided in this embodiment includes the following steps:

[0069] S1, camera preset position setting and camera calibration.

[0070] Specifically, adjust the camera to the appropriate abnormal parking detection position, and set the current camera position as the preset position; then intercept a frame of image of the camera video stream, and perform lane line, ROI, abnormal parking detection area on it calibration.

[0071] S2, convolutional neural network mode...

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Abstract

The invention discloses a real-time detection method for abnormal parking 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 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 static target tracking queues; 7) Abnormal parking detection; 8) Report abnormal parking targets. The present invention proposes a real-time detection algorithm for abnormal parking based on deep learning, which has strong robustness to environmental changes, realizes real-time detection effect and high identification accuracy of abnormal parking.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to a real-time detection method for abnormal parking based on deep learning. Background technique [0002] In recent years, with the popularization of automobiles, there are insufficient parking spaces in cities, and with improper design and complicated roads, parking problems have become extremely prominent. People drive to and from get off work or travel, often because there are not enough parking spaces or it takes too much time to find a free parking space, so the owner chooses to park the car at will. This behavior will not only face fines from the traffic police, but also affect the smooth flow of the road. Abnormal parking will not only affect people's travel efficiency, but will even lead to serious traffic accidents, seriously endangering people's travel safety. Therefore, it is particularly important to perform abnormal parking detection accurately and in real time....

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/32G06N3/04
CPCG06V20/41G06V10/25G06N3/045
Inventor 高飞王金超葛一粟李云阳卢书芳张元鸣邵奇可陆佳炜
Owner ZHEJIANG UNIV OF TECH
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