Traffic jam judgment method based on deep learning

Abstract

The invention discloses a traffic jam judgment method based on deep learning. The traffic jam judgment method comprises the following steps: 1, acquiring a training sample, and adding a tag so as to obtain an image which comprises the tag and corresponds to a monitoring video file; 2, performing forward propagation, namely, transmitting the image which comprises the tag and corresponds to the monitoring video file into a designed convolution neural network model, and performing forward propagation so as to obtain a type tag output by the convolution neural network model; 3, performing back propagation, namely, calculating a loss function value of the type tag output in forward propagation and an actual type tag of the sample, performing back propagation on the loss function value in a minimized error direction so as to adjust a weight matrix of a convolution layer and obtain a final convolution neural network model; 4, judging traffic jam, namely, transmitting at least one frame of image corresponding to a current monitoring video file of a selected road section into the trained final convolution neural network model, and performing forward propagation. By adopting the traffic jam judgment method, the traffic jam grade can be provided according to the traffic situation of a current road, and relatively good applicability and robustness can be achieved.

Description

technical field [0001] The invention relates to a visual image detection data processing technology, in particular to a traffic jam discrimination method based on deep learning. Background technique [0002] With the continuous increase of vehicle occupancy in our country, road congestion is becoming more and more common, which seriously affects people's travel life and brings huge economic losses to the country. If you can make accurate judgments on the current road traffic operation, you can effectively guide and manage the traffic. At present, when using video detection technology to detect traffic congestion, there are two ways: one is to transmit video images to the monitoring center. The other is to select multiple traffic state parameters after obtaining traffic parameters such as traffic flow, road occupancy rate, speed, inter-vehicle distance, queue length, etc., and use the predefined congestion discrimination method to realize the judgment of traffic congestion. ...

AI Technical Summary

Problems solved by technology

[0003] The first method generally uses manual processing, which is inefficient and unable to process more traffic roads; in the second method, the acquisition of various parameters is usually not accurate, so the final processing result will be inaccurate , and this method does not have good scalability

Generally speaking, the prior art has the problem of being unable to accurately and effectively judge road traffic conditions when judging road congestion based on video technology

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