PineSight: The Model Improving Pine Wilt Disease Detection in Illumination-Degraded UAV Imagery

Pine wilt disease (PWD) is a highly destructive forest disease. However, when PWD datasets are constructed, and image detection is conducted, Unmanned Aerial Vehicle (UAV)-based remote sensing detection often encounters the challenge of illumination-degraded UAV imagery. To address this problem, we propose a model called PineSight, which integrates three key enhancements into the Real-Time Detection Transformer (RT-DETR) framework, i.e., Contrastive Language–Image Pre-training (CLIP-LIT), Content-Aware Feature Re-Assembly (CARAFE) and Efficient Vision Transformer (EfficientViT) modules. The proposed model achieves optimal performance across multiple evaluation metrics; for example, the mAP@0.5, mAP@0.5:0.95, Precision and F1-score reach high values of 87.19%, 49.57%, 91.08% and 87.06%, respectively. Simultaneously, the parameter count is reduced from 20.08 M to 11.06 M, and GFLOPs decrease from 58.3 to 28.6. Evaluation on two independent datasets containing images captured under both normal and illumination-degraded conditions shows that PineSight exhibits robustness against illumination degradation. Even under such challenging conditions, the model maintains high performance; it shows high Precision and F1-score with values of 88.10% and 82.23%. The PineSight model not only improves the efficiency of PWD detection but also provides new perspectives and approaches for intelligent forest pest detection in illumination-degraded UAV imagery.