An Enhanced Multi-Scale Feature Perception and Improved Feature Extraction-Based Algorithm for Cotton Pest and Disease Detection

Aiming at the significant variability of cotton leaf pests and diseases in terms of shape, size, and location distribution in the natural environment, as well as the shortcomings of the existing detection models in terms of parameter optimization and detection efficiency, this paper proposes an algorithm for detecting cotton pests and diseases based on enhanced multi-scale feature sensing and improved feature extraction, named SDRM-YOLO.�First, to improve the model's ability to perceive pest and disease feature information and spatial localization accuracy, we propose a Multi-Scale Cross-Space Perception Attention MCPA , which is a mechanism that effectively enhances the model's ability to focus on key target areas by fusing spatial information at multiple scales. Second, to improve the feature extraction quality of the model, we design the C2f-DCN-RCSOSA C2f-DR module, which enables the model to capture the foreground features of the target flexibly and, at the same time, strengthens the focus on the key regions to enhance the feature expression capability. Finally, to reduce the computational complexity of the model and improve the detection speed, we introduce a lightweight network, ShuffleNetv2-RC, into the backbone network to optimize the computational efficiency and maintain a high detection accuracy.�The experimental results show that SDRM-YOLO outperforms other state-of-the-art target detection algorithms on both the Cotton Disease Dataset and Cotton Pest Detect Dataset datasets. Compared with the benchmark model YOLOv8n, the mAP50 metrics were improved by 7.3 and 2.5 , significantly enhancing cotton pest detection's accuracy and robustness.