Traffic violation detection method and system

A detection method and detection system technology, applied in the field of image recognition, can solve problems such as traffic violations and inability to identify drivers, and achieve the effects of reducing traffic accidents, reducing labor costs, and improving intelligence

Inactive Publication Date: 2017-03-22
CHONGQING ZHONGKE YUNCONG TECH CO LTD
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  • Abstract
  • Description
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AI Technical Summary

Problems solved by technology

[0005] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a traffic violation detection m

Method used

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  • Traffic violation detection method and system
  • Traffic violation detection method and system

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Embodiment Construction

[0026] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, in the case of no conflict, the following embodiments and features in the embodiments can be combined with each other.

[0027] It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic ideas of the present invention, and only the components related to the present invention are shown in the diagrams rather than the number, shape and shape of the compo...

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Abstract

The invention provides a traffic violation detection method and system. The method comprises steps of using a deep learning network to establish recognition models based on traffic violations, wherein the recognition models comprise a smoking recognition model, an unbound belt recognition model and a talking-on-cellphone recognition model; obtaining an image in a cab captured during vehicle travel; detecting whether a driver has a traffic violation in the image; and when the driver is found to have a traffic violation, obtaining the position information and the license plate number of a vehicle driven by the driver, and sending the image, the license plate number and the position information corresponding to the vehicle to a traffic police platform. The problem that the traffic violation detection method is single and cannot detect the vehicle internal state is fundamentally solved, behaviors of the driver are standardized, the warning effect is achieved, the manual cost is reduced, the intelligent degree of traffic violation detection is improved, and traffic accidents caused by adverse operations are effectively reduced.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a traffic violation detection method and system. Background technique [0002] With the development of my country's economy, the number of automobiles has increased greatly, and traffic accidents have occurred more and more frequently. Most traffic accidents are caused by traffic accidents, especially serious violations such as running a red light, changing lanes illegally, and going the wrong way. Endangering the safety of people's lives and properties, causing the country to suffer unnecessary heavy economic losses. Therefore, the monitoring of these behaviors has great practical significance. [0003] The current detection of these traffic violations in our country is the induction coil technology, that is, the induction coil is buried at the traffic intersection, and the camera is triggered by the induction coil. This system has the following defects: one is that...

Claims

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Application Information

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IPC IPC(8): G08G1/017G06K9/62G06N3/02
CPCG06N3/02G08G1/0175G06F18/217G06F18/214
Inventor 周曦许林权
Owner CHONGQING ZHONGKE YUNCONG TECH CO LTD
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