{"title":"Detection of Airplanes on the Ground Using YOLO Neural Network","authors":"V. Kharchenko, I. Chyrka","doi":"10.1109/MMET.2018.8460392","DOIUrl":null,"url":null,"abstract":"The presented paper benchmarks the performance of state-of-the-art methods of objects detection in the particular case of airplanes on the ground identification detection in aerial images taken from unmanned aerial vehicles or satellites. There were tested two popular single-stage neural networks YOLO v.3 and Tiny YOLO v.3 based on the “You Only Look Once” approach. The considered artificial neural network architectures for objects detection has been trained and applied over the specifically created image database. Experimental verification proves their high detection ability, location precision and realtime processing speed using modern graphics processing unit. That approach can be easily applied for detection of many different classes of ground objects.","PeriodicalId":343933,"journal":{"name":"2018 IEEE 17th International Conference on Mathematical Methods in Electromagnetic Theory (MMET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"28","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 17th International Conference on Mathematical Methods in Electromagnetic Theory (MMET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMET.2018.8460392","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 28
Abstract
The presented paper benchmarks the performance of state-of-the-art methods of objects detection in the particular case of airplanes on the ground identification detection in aerial images taken from unmanned aerial vehicles or satellites. There were tested two popular single-stage neural networks YOLO v.3 and Tiny YOLO v.3 based on the “You Only Look Once” approach. The considered artificial neural network architectures for objects detection has been trained and applied over the specifically created image database. Experimental verification proves their high detection ability, location precision and realtime processing speed using modern graphics processing unit. That approach can be easily applied for detection of many different classes of ground objects.