DSA-DET: a tea disease detection algorithm based on dynamic spatial pyramid and polarized linear attention

Traditional tea disease detection methods suffer from low efficiency and strong subjectivity, while existing deep learning approaches often demonstrate inadequate detection accuracy and poor real-time performance in complex and variable environments.

Here, we present an intelligent tea disease detection method based on an improved Real-Time Detection Transformer, named DSA-DET. We propose a backbone network based on dynamic attention spatial pyramid modeling to achieve more accurate collaborative modeling of local features and global context. We design an encoder combining polarized linear attention with parallel spatial enhancement networks and multi-scale adaptive enhancement to improve feature extraction capabilities. We further develop an upsampling module employing efficient spatial-channel upsampling and shift mixing mechanisms to enhance the quality of reconstructed features.

Experimental results show that the improved model achieves a precision of 94.73%, a recall of 89.65%, and an mAP⁵⁰ of 93.68%. Compared to the baseline model RT-DETR-R18, its precision is improved by 3.56%, recall by 2.96%, and mAP⁵⁰ by 3.02%; meanwhile, the model maintains a lightweight parameter scale of 15.4M and a real-time detection speed of 71.5 FPS.

The improvement scheme in this study successfully enhances detection accuracy while maintaining a good balance between model complexity and inference speed, providing a practical and reliable technical solution for the intelligent diagnosis of tea diseases.