{"title":"Detection of Deer in Images by Computer Vision Methods","authors":"S. N. Tereshchenko, A. L. Osipov","doi":"10.3103/s8756699024700109","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>An approach to applying machine learning methods for automatic detection of deer individuals in images has been studied. Neural network technology has been used to accurately count the number of deer from photographs. Deep learning methods for convolutional neural networks (ResNet 50, DenseNet, CenterNet, Inception V3, and Xception) were used in conjunction with the transfer learning technique. Based on the faster R-CNN Resnet50 network, a neural network was trained to identify deer individuals from graphic images with an accuracy of 0.91 on a sample using the F1-score metric with a threshold value of 0.6.</p>","PeriodicalId":44919,"journal":{"name":"Optoelectronics Instrumentation and Data Processing","volume":"8 1","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optoelectronics Instrumentation and Data Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3103/s8756699024700109","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
An approach to applying machine learning methods for automatic detection of deer individuals in images has been studied. Neural network technology has been used to accurately count the number of deer from photographs. Deep learning methods for convolutional neural networks (ResNet 50, DenseNet, CenterNet, Inception V3, and Xception) were used in conjunction with the transfer learning technique. Based on the faster R-CNN Resnet50 network, a neural network was trained to identify deer individuals from graphic images with an accuracy of 0.91 on a sample using the F1-score metric with a threshold value of 0.6.
期刊介绍：
The scope of Optoelectronics, Instrumentation and Data Processing encompasses, but is not restricted to, the following areas: analysis and synthesis of signals and images; artificial intelligence methods; automated measurement systems; physicotechnical foundations of micro- and optoelectronics; optical information technologies; systems and components; modelling in physicotechnical research; laser physics applications; computer networks and data transmission systems. The journal publishes original papers, reviews, and short communications in order to provide the widest possible coverage of latest research and development in its chosen field.
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