Unmanned aerial vehicle online aerial vehicle identification and counting method for congested road section

Abstract

The invention discloses an unmanned aerial vehicle online aerial vehicle identification and counting method for a congested road section. The method comprises the following steps: firstly, constructing a congested road section aerial vehicle data set; improving an original YOLOv3 network; using a K-Means + + algorithm to obtain an anchor size for the congested road section aerial vehicle data set,and using the anchor size to train to obtain a congested road section aerial vehicle real-time detection model based on the YOLOv3 convolutional neural network; then, using the obtained vehicle detection model for making real-time on-line detection on aerial images obtained when the unmanned aerial vehicle sails along the congested road section. The vehicle targets are identified, a KM algorithmis adopted to establish the accurate matching relation between the vehicle targets detected by two adjacent frames of images, the number of newly added vehicles of the current frame is accurately obtained compared with the number of newly added vehicles of the previous frame, and therefore congestion road section vehicle number statistics is achieved. The vehicle identification and counting methodprovided by the invention has good flexibility, and realizes accurate, real-time and online statistics of the number of vehicles in a congested road section.

Description

technical field [0001] The invention belongs to the field of intelligent transportation, and in particular relates to a vehicle identification and statistics method for online aerial photography of unmanned aerial vehicles for congested road sections. Background technique [0002] With the continuous increase of my country's traffic mileage and the explosive growth of car ownership, coupled with the complexity of my country's road conditions and traffic conditions, traffic congestion and traffic accidents have occurred frequently. In order to solve the above problems, the research on ITS Intelligent Transportation System (ITS Intelligent Transportation System) has been widely concerned by scholars from all walks of life. Among them, vehicle identification and statistics are important contents of intelligent transportation research, especially for accurate detection and statistics of vehicles in congested road sections, which is helpful for timely and effective adoption of ta...

AI Technical Summary

Problems solved by technology

[0004] In addition, the existing vision-based vehicle recognition and statistical algorithms have low vehicle detection accuracy and poor real-time performance, and they are mainly used for detection and statistics of vehicles in a single frame of pictures, and the number of vehicles for continuous frames is relatively small. And often only the detected vehicle target center point distance is used as the discrimination standard for the same vehicle in consecutive frames, so that it is impossible to accurately distinguish the same vehicle in consecutive frames, which easily causes vehicle statistical errors

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