Novel deep convolution neural network moving vehicle detection method

A deep convolution, neural network technology, applied in instruments, character and pattern recognition, computer parts, etc., can solve problems such as weak system robustness, lack of normal work, accuracy dependent on coverage, etc.

Active Publication Date: 2017-02-15
浙江高信技术股份有限公司
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AI Technical Summary

Problems solved by technology

Usually, the traditional moving vehicle detection method has the following problems: 1) Before extracting the candidate area, the system needs to do a lot of learning on the vehicle pictures in the sample library, and then use the simplified Lucas-Kanade method in the candidate area verification step Tree classification matches hypothetical areas, so the accuracy of the system depends on t

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  • Novel deep convolution neural network moving vehicle detection method
  • Novel deep convolution neural network moving vehicle detection method
  • Novel deep convolution neural network moving vehicle detection method

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Embodiment Construction

[0059] The present invention will be further described below in conjunction with the accompanying drawings.

[0060] The invention uses a convolutional neural network method combined with machine learning technology to detect the moving vehicle ahead. The specific scene is attached figure 1 As shown, the own car 1 and the front car 2 with the front camera are driving on the road at speeds v1 and v2 respectively, and the distance between the cars is S. Detect moving vehicles in the video. In order to effectively detect moving vehicles ahead, this method constructs a new detection framework as shown in the attached figure 2 , and build a specific convolutional neural network LetNet-5, the convolution kernel used in the convolutional neural network structure is only used to extract vehicle features, and no longer extract the rest of the object features (such as houses, sky and trees, etc.). Among them, the convolution kernel is five 5×5 matrix blocks obtained through training...

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Abstract

The invention relates to a moving vehicle detection method based on a novel deep convolution neural network. The method uses a monocular camera to implement a detection algorithm of a moving vehicle in the front and brings forward a moving vehicle detection framework based a novel convolution neural network. Through the novel convolution neural network, vehicle features can be obtained very accurately, and a target vehicle can be further accurately separated out, so that a machine recognition effect can be achieved, and the target vehicle can be tracked more quickly. As far as vehicle detection is concerned, the method can be adapted to a high-speed driving environment, and provides technical guarantee for implementation of intelligent assisted driving. The method solves the problem of traffic safety, improves road vehicle throughput, reduces the malignant traffic accident rate, and reduces life and property losses. From the perspective of improving social and economic benefits, the method has great meaning in reality and broad application prospect.

Description

technical field [0001] The invention belongs to the technical field of automobile collision avoidance, and relates to a recognition method for moving vehicles, in particular to an automobile auxiliary driving technology using a monocular camera, which realizes detection and tracking of moving vehicles. Background technique [0002] As a modern and advanced means of transportation, automobiles have changed people's way of life, promoted the development of social economy and the progress of human culture, brought great convenience to people's life, but also brought serious traffic safety problems. In order to reduce traffic accidents and casualties, countries are actively researching countermeasures and using various methods and measures to reduce the occurrence of traffic accidents. Not only that, the assisted driving system of the car is closely related to the future development direction of the car. In the near future, car driving will become simple and convenient, and the ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46
CPCG06V20/584G06V10/40G06V2201/08
Inventor 高生扬姜显扬唐向宏严军荣姚英彪许晓荣
Owner 浙江高信技术股份有限公司
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