Forest pest monitoring and early warning using UAV remote sensing and computer vision techniques - Xiaoyu Li; AChuan Wang (Scientific Reports)
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.