Real-time vehicle detection method based on unmanned aerial vehicle platform

Abstract

The invention discloses a real-time vehicle detection method based on an unmanned aerial vehicle platform, and the method comprises the steps: building an aerial vehicle data set through photographingof an unmanned aerial vehicle, and dividing the whole data set into a training set and a test set according to a certain proportion; establishing a fast elimination convolutional layer of the convolutional neural network; establishing a multi-scale convolution layer of the neural network; carrying out multi-scale anchor point design based on the aspect ratio of the vehicle in the aerial video, and carrying out densification processing on small-scale anchor points; based on a binary weight network, performing time optimization on the network; loading a video data set, and training the convolutional neural network; and detecting the vehicle in the video in real time in the aerial video of the unmanned aerial vehicle. According to the method, the vehicle can be detected in the moving background, the method is suitable for the aerial photography environment of the unmanned aerial vehicle, the omission ratio of the small target vehicle is greatly reduced by reasonably designing the step length of the RDCL layer and adjusting the aspect ratio of the anchor point, and the vehicle in the aerial photography video can be detected in real time on the airborne computing module.

Description

technical field [0001] The invention belongs to the field of video image processing, and relates to a real-time vehicle detection method based on an unmanned aerial vehicle platform. Background technique [0002] With the continuous improvement of economic development and people's living standards, the number of automobiles in our country continues to increase. According to statistics from the Ministry of Public Security, as of the end of 2018, the number of motor vehicles in the country reached 325 million, an increase of 15.56 million compared with the end of 2017, and the number of motor vehicle drivers reached 407 million, an increase of 2.23 million compared with the end of 2017. . At the same time, traffic congestion, traffic accidents, and deterioration of the traffic environment have gradually become common problems in various cities. In order to alleviate the increasingly serious traffic problems, the development of intelligent transportation systems and the use o...

AI Technical Summary

Problems solved by technology

However, the analysis and processing based on the backhauled monitoring video has delay and lag, and cannot reflect the traffic environment and vehicle status in time. Using deep learning technology on the airborne computing device to directly analyze and process the monitoring video can solve the problem of video backhaul. The delay caused by the real-time detection of aerial vehicles

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