Improving the model of object detection on aerial photographs and video in unmanned aerial systems

Eastern-European Journal of Enterprise Technologies

Vadym Slyusar¹  Mykhailo Protsenko¹  Oleh Biloborodov¹  Vasyl Melkin¹  Halyna Kalynychenko²  Mykola Samoilenko²  Olena Kravchenko²  Anton Rohovyi³  Mykhaylo Soloshchuk³  Anton Chernukha⁴ 

[1] Central Scientific Research Institute of Armament and Military Equipment of the Armed Forces of Ukraine;Mykolayiv National Agrarian University;National Technical University "Kharkiv Polytechnic Institute";National University of Civil Defence of Ukraine;

关键词: neural network;    object detection;    visdrone 2021;    microsoft coco;    yolov5x;    unmanned aerial system;   

DOI  :  10.15587/1729-4061.2022.252876

来源: DOAJ

【 摘 要 】

This paper considers a model of object detection on aerial photographs and video using a neural network in unmanned aerial systems. The development of artificial intelligence and computer vision systems for unmanned systems (drones, robots) requires the improvement of models for detecting and recognizing objects in images and video streams. The results of video and aerial photography in unmanned aircraft systems are processed by the operator manually but there are objective difficulties associated with the operator’s processing of a large number of videos and aerial photographs, so it is advisable to automate this process. Analysis of neural network models has revealed that the YOLOv5x model (USA) is most suitable, as a basic model, for performing the task of object detection on aerial photographs and video. The Microsoft COCO suite (USA) is used to train this model. This set contains more than 200,000 images across 80 categories. To improve the YOLOv5x model, the neural network was trained with a set of VisDrone 2021 images (China) with the choice of such optimal training parameters as the optimization algorithm SGD; the initial learning rate (step) of 0.0005; the number of epochs of 25. As a result, a new model of object detection on aerial photographs and videos with the proposed name VisDroneYOLOv5x was obtained. The effectiveness of the improved model was studied using aerial photographs and videos from the VisDrone 2021 set. To assess the effectiveness of the model, the following indicators were chosen as the main indicators: accuracy, sensitivity, the estimation of average accuracy. Using a convolutional neural network has made it possible to automate the process of object detection on aerial photographs and video in unmanned aerial systems.

【 授权许可】

Unknown