Deep convolution neural network-based traffic flow density estimation method

Abstract

The present invention provides a deep convolution neural network-based traffic flow density estimation method. The method comprises the steps of collecting road video images by a camera, sending multi-scale pyramid image blocks into a convolution neural network through image pre-processing, extracting simple features and abstract features from a bottom layer to a high layer, and obtaining a distribution density image of each scale of traffic flow image; learning a mapping from the multi-scale distribution density image to a distribution density image of an overall image and a number of all vehicles in the image by using a fully connected network layer; dividing regions of interest of a distribution density image of a video image output by the convolution neural network, summing pixels of the regions of interest so as to obtain a number of vehicles of a single lane or multiple lanes; and calculating according to a region length so as to obtain an instantaneous traffic flow density of the region. Through adoption of the method, vehicles are calculated and traffic flow density is estimated in real time more accurately.

Description

technical field [0001] The invention relates to a method for estimating traffic flow density based on a deep convolutional neural network, which belongs to the technical field of intelligent transportation. Background technique [0002] With the development of science and technology and the improvement of people's living standards, cars have become an indispensable means of transportation for people to travel and transport. All kinds of motor vehicles can be seen everywhere on the road. While automobile traffic makes people feel the convenience and comfort of modern life, it also reflects the lag and limitation of road facilities. Traffic congestion will bring a series of problems that cannot be ignored, such as long waiting time for vehicles, traffic accidents, and environmental pollution. In order to solve the above problems, intelligent transportation came into being. Vehicle counting and traffic density detection are important contents of intelligent transportation. T...

AI Technical Summary

Problems solved by technology

Although the invention application does not require the extraction of vehicle targets, it cannot obtain the specific number of vehicles at the same time, and can only judge the traffic density qualitatively, lacking the accuracy of traffic density detection and the ability of quantitative analysis

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