Overview and Comparison of Deep Neural Networks for Wildlife Recognition Using Infrared Images
There are multiple uses for single-channel images, such as infrared imagery, depth maps, and others. To automatically classify objects in such images, an algorithm suited for single-channel image processing is required. This study explores the application of deep learning techniques for the recognit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/5/4/135 |
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| author | Peter Sykora Patrik Kamencay Roberta Hlavata Robert Hudec |
| author_facet | Peter Sykora Patrik Kamencay Roberta Hlavata Robert Hudec |
| author_sort | Peter Sykora |
| collection | DOAJ |
| description | There are multiple uses for single-channel images, such as infrared imagery, depth maps, and others. To automatically classify objects in such images, an algorithm suited for single-channel image processing is required. This study explores the application of deep learning techniques for the recognition of wild animals using infrared images. Traditional methods of wildlife monitoring often rely on visible light imaging, which can be hindered by various environmental factors such as darkness, fog, and dense foliage. In contrast, infrared imaging captures the thermal signatures of animals, providing a robust alternative for wildlife detection and identification. We test a Convolutional Neural Network (CNN) model specifically designed to analyze infrared images, leveraging the unique thermal patterns emitted by different animal species. The model is trained and tested on a diverse dataset of infrared images, demonstrating high accuracy in distinguishing between multiple species. In this paper, we also present a comparison of several well-known artificial neural networks on this data. To ensure accurate testing, we introduce a new dataset containing infrared photos of Slovak wildlife, specifically including classes such as bear, deer, boar, and fox. To complement this dataset, the Fashion MNIST dataset was also used. Our results indicate that deep learning approaches significantly enhance the capability of infrared imaging for wildlife monitoring, offering a reliable and efficient tool for conservation efforts and ecological studies. |
| format | Article |
| id | doaj-art-17bab3f6d6be48dab2a867a49f10a916 |
| institution | Kabale University |
| issn | 2673-2688 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-17bab3f6d6be48dab2a867a49f10a9162024-12-27T14:05:06ZengMDPI AGAI2673-26882024-12-01542801282810.3390/ai5040135Overview and Comparison of Deep Neural Networks for Wildlife Recognition Using Infrared ImagesPeter Sykora0Patrik Kamencay1Roberta Hlavata2Robert Hudec3Department of Multimedia and Information-Communication Technologies, University of Zilina, 010 26 Zilina, SlovakiaDepartment of Multimedia and Information-Communication Technologies, University of Zilina, 010 26 Zilina, SlovakiaDepartment of Multimedia and Information-Communication Technologies, University of Zilina, 010 26 Zilina, SlovakiaDepartment of Multimedia and Information-Communication Technologies, University of Zilina, 010 26 Zilina, SlovakiaThere are multiple uses for single-channel images, such as infrared imagery, depth maps, and others. To automatically classify objects in such images, an algorithm suited for single-channel image processing is required. This study explores the application of deep learning techniques for the recognition of wild animals using infrared images. Traditional methods of wildlife monitoring often rely on visible light imaging, which can be hindered by various environmental factors such as darkness, fog, and dense foliage. In contrast, infrared imaging captures the thermal signatures of animals, providing a robust alternative for wildlife detection and identification. We test a Convolutional Neural Network (CNN) model specifically designed to analyze infrared images, leveraging the unique thermal patterns emitted by different animal species. The model is trained and tested on a diverse dataset of infrared images, demonstrating high accuracy in distinguishing between multiple species. In this paper, we also present a comparison of several well-known artificial neural networks on this data. To ensure accurate testing, we introduce a new dataset containing infrared photos of Slovak wildlife, specifically including classes such as bear, deer, boar, and fox. To complement this dataset, the Fashion MNIST dataset was also used. Our results indicate that deep learning approaches significantly enhance the capability of infrared imaging for wildlife monitoring, offering a reliable and efficient tool for conservation efforts and ecological studies.https://www.mdpi.com/2673-2688/5/4/135deep learningneural networkanimal recognitioninfrared images |
| spellingShingle | Peter Sykora Patrik Kamencay Roberta Hlavata Robert Hudec Overview and Comparison of Deep Neural Networks for Wildlife Recognition Using Infrared Images AI deep learning neural network animal recognition infrared images |
| title | Overview and Comparison of Deep Neural Networks for Wildlife Recognition Using Infrared Images |
| title_full | Overview and Comparison of Deep Neural Networks for Wildlife Recognition Using Infrared Images |
| title_fullStr | Overview and Comparison of Deep Neural Networks for Wildlife Recognition Using Infrared Images |
| title_full_unstemmed | Overview and Comparison of Deep Neural Networks for Wildlife Recognition Using Infrared Images |
| title_short | Overview and Comparison of Deep Neural Networks for Wildlife Recognition Using Infrared Images |
| title_sort | overview and comparison of deep neural networks for wildlife recognition using infrared images |
| topic | deep learning neural network animal recognition infrared images |
| url | https://www.mdpi.com/2673-2688/5/4/135 |
| work_keys_str_mv | AT petersykora overviewandcomparisonofdeepneuralnetworksforwildliferecognitionusinginfraredimages AT patrikkamencay overviewandcomparisonofdeepneuralnetworksforwildliferecognitionusinginfraredimages AT robertahlavata overviewandcomparisonofdeepneuralnetworksforwildliferecognitionusinginfraredimages AT roberthudec overviewandcomparisonofdeepneuralnetworksforwildliferecognitionusinginfraredimages |