YOLOv8-RD: High-Robust Pine Wilt Disease Detection Method Based on Residual Fuzzy YOLOv8
Pine wilt disease (PWD) poses a severe threat to the health of pine trees and has resulted in substantial losses to global pine forest resources. Due to the minute size of the pathogens and the concealed symptoms of PWD, early detection through remote sensing image technology is essential. However,...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10750037/ |
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| author | Junchao Yuan Lina Wang Tingting Wang Ali Kashif Bashir Maryam M. Al Dabel Jiaxing Wang Hailin Feng Kai Fang Wei Wang |
| author_facet | Junchao Yuan Lina Wang Tingting Wang Ali Kashif Bashir Maryam M. Al Dabel Jiaxing Wang Hailin Feng Kai Fang Wei Wang |
| author_sort | Junchao Yuan |
| collection | DOAJ |
| description | Pine wilt disease (PWD) poses a severe threat to the health of pine trees and has resulted in substantial losses to global pine forest resources. Due to the minute size of the pathogens and the concealed symptoms of PWD, early detection through remote sensing image technology is essential. However, in practical applications, remote sensing images are easily affected by factors, such as cloud cover and changes in illumination, resulting in significant noise and blurriness in the images. These interference factors significantly reduce the accuracy of existing object detection models. Therefore, this article presents a novel and highly robust methodology for detecting PWD, termed YOLOv8-RD. We synthesized the benefits of residual learning and fuzzy deep neural networks to develop a residual fuzzy module (ResFuzzy), which adeptly filters image noise and refines background features with enhanced smoothness. Simultaneously, we integrated a detail processing module into the ResFuzzy module to enhance the low-frequency detail features transmitted in residual learning. Furthermore, by incorporating the dynamic upsampling operator, our model can dynamically adjust the sampling step size based on the variations in the input feature map during the upsampling process, thereby effectively recovering detail from the feature map. Our model exhibited exceptional robustness to severe noise. When evaluated on a PWD dataset with 100% interference samples at an intensity of 0.07, our model achieved an average precision improvement of 4.9%, 6.3%, 7.3%, and 3.0% compared to four most representative models, making it well suited for PWD detection in interfering environments. |
| format | Article |
| id | doaj-art-9ffdd527606b4bcd99c5ec8cd96fd00c |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-9ffdd527606b4bcd99c5ec8cd96fd00c2024-11-29T00:00:52ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-011838539710.1109/JSTARS.2024.349483810750037YOLOv8-RD: High-Robust Pine Wilt Disease Detection Method Based on Residual Fuzzy YOLOv8Junchao Yuan0https://orcid.org/0009-0009-9368-6606Lina Wang1https://orcid.org/0009-0009-7601-2917Tingting Wang2https://orcid.org/0000-0002-2666-7159Ali Kashif Bashir3https://orcid.org/0000-0003-2601-9327Maryam M. Al Dabel4https://orcid.org/0000-0003-4371-8939Jiaxing Wang5https://orcid.org/0000-0002-7672-6911Hailin Feng6https://orcid.org/0000-0003-2734-480XKai Fang7https://orcid.org/0000-0003-0419-1468Wei Wang8https://orcid.org/0000-0002-1717-5785College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, ChinaCollege of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, ChinaCollege of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, ChinaDepartment of Computing and Mathematics, Manchester Metropolitan University, Manchester, U.K.Department of Computer Science and Engineering, University of Hafr AlBatin, Hafar Al Batin, Saudi ArabiaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, ChinaCollege of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, ChinaCollege of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, ChinaGuangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing, Artificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen, ChinaPine wilt disease (PWD) poses a severe threat to the health of pine trees and has resulted in substantial losses to global pine forest resources. Due to the minute size of the pathogens and the concealed symptoms of PWD, early detection through remote sensing image technology is essential. However, in practical applications, remote sensing images are easily affected by factors, such as cloud cover and changes in illumination, resulting in significant noise and blurriness in the images. These interference factors significantly reduce the accuracy of existing object detection models. Therefore, this article presents a novel and highly robust methodology for detecting PWD, termed YOLOv8-RD. We synthesized the benefits of residual learning and fuzzy deep neural networks to develop a residual fuzzy module (ResFuzzy), which adeptly filters image noise and refines background features with enhanced smoothness. Simultaneously, we integrated a detail processing module into the ResFuzzy module to enhance the low-frequency detail features transmitted in residual learning. Furthermore, by incorporating the dynamic upsampling operator, our model can dynamically adjust the sampling step size based on the variations in the input feature map during the upsampling process, thereby effectively recovering detail from the feature map. Our model exhibited exceptional robustness to severe noise. When evaluated on a PWD dataset with 100% interference samples at an intensity of 0.07, our model achieved an average precision improvement of 4.9%, 6.3%, 7.3%, and 3.0% compared to four most representative models, making it well suited for PWD detection in interfering environments.https://ieeexplore.ieee.org/document/10750037/Fuzzy deep neural networks (FDNNs)interference environmentsremote sensingresidual learningupsampling process |
| spellingShingle | Junchao Yuan Lina Wang Tingting Wang Ali Kashif Bashir Maryam M. Al Dabel Jiaxing Wang Hailin Feng Kai Fang Wei Wang YOLOv8-RD: High-Robust Pine Wilt Disease Detection Method Based on Residual Fuzzy YOLOv8 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Fuzzy deep neural networks (FDNNs) interference environments remote sensing residual learning upsampling process |
| title | YOLOv8-RD: High-Robust Pine Wilt Disease Detection Method Based on Residual Fuzzy YOLOv8 |
| title_full | YOLOv8-RD: High-Robust Pine Wilt Disease Detection Method Based on Residual Fuzzy YOLOv8 |
| title_fullStr | YOLOv8-RD: High-Robust Pine Wilt Disease Detection Method Based on Residual Fuzzy YOLOv8 |
| title_full_unstemmed | YOLOv8-RD: High-Robust Pine Wilt Disease Detection Method Based on Residual Fuzzy YOLOv8 |
| title_short | YOLOv8-RD: High-Robust Pine Wilt Disease Detection Method Based on Residual Fuzzy YOLOv8 |
| title_sort | yolov8 rd high robust pine wilt disease detection method based on residual fuzzy yolov8 |
| topic | Fuzzy deep neural networks (FDNNs) interference environments remote sensing residual learning upsampling process |
| url | https://ieeexplore.ieee.org/document/10750037/ |
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