RDD-DETR Algorithm for Full-Scale Detection of Rice Diseases

To tackle the challenges of high computational expense, limited detection accuracy, and imbalanced detection performance across multi-scale targets in rice disease identification within complex natural environments, we propose the Rice Disease Deformable Detection Transformer (RDD-DETR). This model serves as a full-scale detection framework based on the Deformable Detection Transformer (Deformable DETR). The model introduces a Rectified Linear Unit (ReLU)-enhanced lightweight linear attention module, which uses differentiated position coding and ReLU kernel mapping to reduce computational complexity. A cross-layer dynamic fusion and inter-layer supervision module is designed to break the serial dependence in decoders and strengthen interlayer supervision, enabling the decoder to generate more accurate and robust target representations. Furthermore, we design an optimization mechanism for sub-scale positioning loss to substantially boost detection accuracy across all target scales. Experiments on our custom RiceLeafDisease-RSOD dataset demonstrate that RDD-DETR achieves an average precision (AP) at Intersection over Union (IoU) threshold 0.5:0.95 of 0.7363 across all categories, surpassing the baseline model by 6.09%. Notably, detection accuracy improves by 6.10% for small targets, 6.61% for medium targets, and 5.42% for large targets. Evaluated on the validation set (671 images with 2482 labeled bounding boxes), the model achieves an AP at IoU threshold 0.5 of 0.9684 while reducing computational cost by 37.41% (from 136.02 to 85.1 Giga Floating Point Operations, GFLOPs) compared to the original Deformable DETR. These results validate RDD-DETR as an effective solution for accurate and efficient real-time rice disease monitoring in complex field environments.