A real-time vehicle detection method based on UAV platform

Abstract

The invention discloses a real-time vehicle detection method based on an unmanned aerial vehicle platform. Aerial photography vehicle data set is established through unmanned aerial vehicle shooting, and the whole data set is divided into training set and test set according to a certain proportion; Convolutional layer; build a multi-scale convolutional layer of neural network; design multi-scale anchor points based on the aspect ratio of vehicles in aerial video, and densify small-scale anchor points; based on binary weight network; Time optimization; loading video datasets to train convolutional neural networks; real-time detection of vehicles in video from drone aerial video. The invention can detect vehicles in the background of motion, and is suitable for the environment of drone aerial photography. The missed detection rate of small target vehicles is greatly reduced by reasonably designing the step length of the RDCL layer and adjusting the aspect ratio of the anchor point. Vehicles in aerial video can be detected in real time on the onboard 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 economic development and the continuous improvement of people's living standards, the number of automobiles in my country is increasing. According to the statistics of the Ministry of Public Security, by 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 cities. In order to alleviate the increasingly serious traffic problems, the development of intelligent transportation systems and the use of various new ...

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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