Forest pest monitoring and early warning using UAV remote sensing and computer vision techniques
Abstract Unmanned aerial vehicle (UAV) remote sensing has revolutionized forest pest monitoring and early warning systems. However, the susceptibility of UAV-based object detection models to adversarial attacks raises concerns about their reliability and robustness in real-world deployments. To addr...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84464-3 |
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| author | Xiaoyu Li AChuan Wang |
| author_facet | Xiaoyu Li AChuan Wang |
| author_sort | Xiaoyu Li |
| collection | DOAJ |
| description | Abstract Unmanned aerial vehicle (UAV) remote sensing has revolutionized forest pest monitoring and early warning systems. However, the susceptibility of UAV-based object detection models to adversarial attacks raises concerns about their reliability and robustness in real-world deployments. To address this challenge, we propose SC-RTDETR, a novel framework for secure and robust object detection in forest pest monitoring using UAV imagery. SC-RTDETR integrates a soft-thresholding adaptive filtering module and a cascaded group attention mechanism into the Real-time Detection Transformer (RTDETR) architecture, significantly enhancing its resilience against adversarial perturbations. Extensive experiments on a real-world pine wilt disease dataset demonstrate the superior performance of SC-RTDETR, with an improvement of 7.1% in mean Average Precision (mAP) and 6.5% in F1-score under strong adversarial attack conditions compared to state-of-the-art methods. The ablation studies and visualizations provide insights into the effectiveness of the proposed components, validating their contributions to the overall robustness and performance of SC-RTDETR. Our framework offers a promising solution for accurate and reliable forest pest monitoring in non-secure environments. |
| format | Article |
| id | doaj-art-9bfc61db43d0450ea4976ecb6baea1b0 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9bfc61db43d0450ea4976ecb6baea1b02025-01-05T12:17:48ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-024-84464-3Forest pest monitoring and early warning using UAV remote sensing and computer vision techniquesXiaoyu Li0AChuan Wang1College of Computer and Control Engineering, Northeast Forestry UniversityCollege of Computer and Control Engineering, Northeast Forestry UniversityAbstract Unmanned aerial vehicle (UAV) remote sensing has revolutionized forest pest monitoring and early warning systems. However, the susceptibility of UAV-based object detection models to adversarial attacks raises concerns about their reliability and robustness in real-world deployments. To address this challenge, we propose SC-RTDETR, a novel framework for secure and robust object detection in forest pest monitoring using UAV imagery. SC-RTDETR integrates a soft-thresholding adaptive filtering module and a cascaded group attention mechanism into the Real-time Detection Transformer (RTDETR) architecture, significantly enhancing its resilience against adversarial perturbations. Extensive experiments on a real-world pine wilt disease dataset demonstrate the superior performance of SC-RTDETR, with an improvement of 7.1% in mean Average Precision (mAP) and 6.5% in F1-score under strong adversarial attack conditions compared to state-of-the-art methods. The ablation studies and visualizations provide insights into the effectiveness of the proposed components, validating their contributions to the overall robustness and performance of SC-RTDETR. Our framework offers a promising solution for accurate and reliable forest pest monitoring in non-secure environments.https://doi.org/10.1038/s41598-024-84464-3UAV remote sensingForest pest monitoringEarly warningObject detectionAdversarial attacksSoft-thresholding adaptive filtering |
| spellingShingle | Xiaoyu Li AChuan Wang Forest pest monitoring and early warning using UAV remote sensing and computer vision techniques Scientific Reports UAV remote sensing Forest pest monitoring Early warning Object detection Adversarial attacks Soft-thresholding adaptive filtering |
| title | Forest pest monitoring and early warning using UAV remote sensing and computer vision techniques |
| title_full | Forest pest monitoring and early warning using UAV remote sensing and computer vision techniques |
| title_fullStr | Forest pest monitoring and early warning using UAV remote sensing and computer vision techniques |
| title_full_unstemmed | Forest pest monitoring and early warning using UAV remote sensing and computer vision techniques |
| title_short | Forest pest monitoring and early warning using UAV remote sensing and computer vision techniques |
| title_sort | forest pest monitoring and early warning using uav remote sensing and computer vision techniques |
| topic | UAV remote sensing Forest pest monitoring Early warning Object detection Adversarial attacks Soft-thresholding adaptive filtering |
| url | https://doi.org/10.1038/s41598-024-84464-3 |
| work_keys_str_mv | AT xiaoyuli forestpestmonitoringandearlywarningusinguavremotesensingandcomputervisiontechniques AT achuanwang forestpestmonitoringandearlywarningusinguavremotesensingandcomputervisiontechniques |