Sidewalk vehicle illegal parking detection method based on target detection and semantic segmentation

A semantic segmentation and target detection technology, applied in the field of deep learning and computer vision, can solve problems such as poor real-time performance, high misjudgment rate, and inapplicability to rotating cameras, etc., to solve the problem of segmented area failure, good real-time performance, and improved The effect of judgment accuracy

Active Publication Date: 2020-07-03
成都市微泊科技有限公司 +1
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Problems solved by technology

[0007] The purpose of the present invention is to provide a sidewalk vehicle illegal parking detection method based on target detection and semantic segmentation, which solves the problem of high misjudgment rate and poor real-time performance in the existing vehic...

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  • Sidewalk vehicle illegal parking detection method based on target detection and semantic segmentation
  • Sidewalk vehicle illegal parking detection method based on target detection and semantic segmentation
  • Sidewalk vehicle illegal parking detection method based on target detection and semantic segmentation

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Embodiment 1

[0052] A preferred embodiment of the present invention provides a method for detecting illegal parking of sidewalk vehicles based on target detection and semantic segmentation, comprising the following steps:

[0053] 1. Training stage:

[0054] Step 1: Collect surveillance photos from surveillance cameras in different scenarios, and perform semantic segmentation annotations to obtain a semantic segmentation dataset, train the semantic segmentation network with the semantic segmentation dataset, and obtain a semantic segmentation model;

[0055] Specifically, in this embodiment, 533 monitoring photos of Skynet cameras are selected and divided into training sets and test sets. The training set is used for training semantic segmentation, and the test set is used to verify the effect of semantic segmentation, and then semantic segmentation is marked. , specifically to mark the semantic segmentation training set as sidewalk (sidewalk) and road (road) two categories, as the urban r...

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Abstract

The invention discloses a sidewalk vehicle illegal parking detection method based on target detection and semantic segmentation, and belongs to the field of deep learning and computer vision, and themethod comprises the steps: respectively training a semantic segmentation network and a target detection network at a training stage, and obtaining a semantic segmentation model and a target detectionmodel; the method comprises the following steps of: extracting an urban road background image by using a Gaussian mixture background model; the method comprises the following steps: acquiring a semantic segmentation model, performing semantic segmentation by using a semantic segmentation algorithm and the semantic segmentation model to obtain a semantic segmentation graph, judging whether a camera rotates or not by using a perceptual hash algorithm, performing vehicle detection by using a target detection algorithm and the target detection model, marking a vehicle detection frame, and finallycomparing the vehicle detection frame with the semantic segmentation graph. The problems that an existing vehicle illegal parking detection method is high in misjudgment rate and poor in real-time performance, a sidewalk illegal parking area needs to be manually calibrated, and when a camera rotates, the illegal parking area needs to be manually calibrated again, so that the camera is not suitable for rotating are solved.

Description

technical field [0001] The invention belongs to the field of deep learning and computer vision, and relates to a method for detecting illegal parking of sidewalk vehicles based on target detection and semantic segmentation. Background technique [0002] With the rapid development of economy and urbanization, the total number of roads and vehicles in various cities in my country continues to increase, and the illegal parking of vehicles is also increasing. The violation detection of vehicles in urban road monitoring images or videos has become an important task in urban management. an important task. Although high-definition surveillance cameras have been deployed at most intersections, the amount of video generated every day is increasing. Manual real-time video monitoring or offline processing is time-consuming and laborious, and is prone to delays and omissions. Therefore, it is urgent to find an efficient The method assists manual monitoring and processing. [0003] Ille...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/54G06V10/267G06V2201/08G06N3/045G06F18/241
Inventor 熊运余赵逸如何梦园
Owner 成都市微泊科技有限公司
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