Remote Monitoring of Amur Tigers in Forest Ecosystems Using Improved YOLOX Algorithm
In response to the challenge of collecting behavioral data on <i>Amur tigers</i> living in forests, a remote real-time data collection approach is proposed. In this article, a novel attention mechanism named CBAM-E is introduced, and CBAM-E as well as the CIoU loss function are incorpora...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/14/10/2000 |
| _version_ | 1797573826743107584 |
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| author | Yonghua Xie Wenhua Yu |
| author_facet | Yonghua Xie Wenhua Yu |
| author_sort | Yonghua Xie |
| collection | DOAJ |
| description | In response to the challenge of collecting behavioral data on <i>Amur tigers</i> living in forests, a remote real-time data collection approach is proposed. In this article, a novel attention mechanism named CBAM-E is introduced, and CBAM-E as well as the CIoU loss function are incorporated into the YOLOX object detection algorithm, resulting in a new YOLOX model. The new model demonstrates significant performance improvements over the original model, with the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>m</mi><mi>A</mi><mi>P</mi></mrow><mrow><mn>0.5</mn></mrow></msub></mrow></semantics></math></inline-formula> detection accuracy metric rising from 97.32 to 98.18%, indicating a boost of 0.86%, and the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>m</mi><mi>A</mi><mi>P</mi></mrow><mrow><mn>0.75</mn></mrow></msub></mrow></semantics></math></inline-formula> metric increasing from 75.10 to 78.70%, marking an enhancement of 3.60%. The enhanced algorithm is subsequently applied to remote terminal information collection, offering a reference for detection algorithms in the study of wild behaviors of <i>Amur tigers</i> in forests, biodiversity conservation, and the collection of related field data about <i>Amur tigers</i> in the wild. |
| first_indexed | 2024-03-10T21:14:32Z |
| format | Article |
| id | doaj.art-40506ce1cc2e4e2cb423fd8bed51c820 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T21:14:32Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-40506ce1cc2e4e2cb423fd8bed51c8202023-11-19T16:32:18ZengMDPI AGForests1999-49072023-10-011410200010.3390/f14102000Remote Monitoring of Amur Tigers in Forest Ecosystems Using Improved YOLOX AlgorithmYonghua Xie0Wenhua Yu1College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, ChinaIn response to the challenge of collecting behavioral data on <i>Amur tigers</i> living in forests, a remote real-time data collection approach is proposed. In this article, a novel attention mechanism named CBAM-E is introduced, and CBAM-E as well as the CIoU loss function are incorporated into the YOLOX object detection algorithm, resulting in a new YOLOX model. The new model demonstrates significant performance improvements over the original model, with the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>m</mi><mi>A</mi><mi>P</mi></mrow><mrow><mn>0.5</mn></mrow></msub></mrow></semantics></math></inline-formula> detection accuracy metric rising from 97.32 to 98.18%, indicating a boost of 0.86%, and the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>m</mi><mi>A</mi><mi>P</mi></mrow><mrow><mn>0.75</mn></mrow></msub></mrow></semantics></math></inline-formula> metric increasing from 75.10 to 78.70%, marking an enhancement of 3.60%. The enhanced algorithm is subsequently applied to remote terminal information collection, offering a reference for detection algorithms in the study of wild behaviors of <i>Amur tigers</i> in forests, biodiversity conservation, and the collection of related field data about <i>Amur tigers</i> in the wild.https://www.mdpi.com/1999-4907/14/10/2000YOLOXwildlife conservationobject detectionremote terminal detection<i>Amur tiger</i> |
| spellingShingle | Yonghua Xie Wenhua Yu Remote Monitoring of Amur Tigers in Forest Ecosystems Using Improved YOLOX Algorithm Forests YOLOX wildlife conservation object detection remote terminal detection <i>Amur tiger</i> |
| title | Remote Monitoring of Amur Tigers in Forest Ecosystems Using Improved YOLOX Algorithm |
| title_full | Remote Monitoring of Amur Tigers in Forest Ecosystems Using Improved YOLOX Algorithm |
| title_fullStr | Remote Monitoring of Amur Tigers in Forest Ecosystems Using Improved YOLOX Algorithm |
| title_full_unstemmed | Remote Monitoring of Amur Tigers in Forest Ecosystems Using Improved YOLOX Algorithm |
| title_short | Remote Monitoring of Amur Tigers in Forest Ecosystems Using Improved YOLOX Algorithm |
| title_sort | remote monitoring of amur tigers in forest ecosystems using improved yolox algorithm |
| topic | YOLOX wildlife conservation object detection remote terminal detection <i>Amur tiger</i> |
| url | https://www.mdpi.com/1999-4907/14/10/2000 |
| work_keys_str_mv | AT yonghuaxie remotemonitoringofamurtigersinforestecosystemsusingimprovedyoloxalgorithm AT wenhuayu remotemonitoringofamurtigersinforestecosystemsusingimprovedyoloxalgorithm |