A deep convolutional neural network moving vehicle detection method

Abstract

The invention relates to a moving vehicle detection method based on a deep convolutional neural network. The invention utilizes a monocular camera to realize a detection algorithm for a moving vehicle in front, and proposes a moving vehicle detection framework based on a convolutional neural network. Through the convolutional network, the characteristics of the vehicle can be obtained very accurately, and then the target vehicle can be accurately separated to achieve the effect of machine recognition, so that the target vehicle can be tracked more quickly. And in terms of vehicle detection, it can adapt to the high-speed driving environment, providing technical support for the realization of intelligent assisted driving. The invention not only solves the problem of traffic safety, improves road traffic throughput, reduces the occurrence rate of vicious traffic accidents, but also reduces the loss of life and property. In terms of improving social and economic benefits, this invention has great practical significance and broad application prospects.

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 ...

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 the coverage of sample images; 2) This method is mainly aimed at the detection and tracking of single-target vehicles, and the robustness of the system is not strong in practical applications. Practical; 3) The premise of the normal detection work of the detection system is that the light is good and does not have complex terrain, and it does not have the ability to work normally in the dark

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