Cotton Pest Detection Method based on Improved YOLOv8
DOI:
https://doi.org/10.54097/mz27vb83Keywords:
Object Detection, YOLOv8, Attention Mechanism, Multi-scale Feature Fusion, Agricultural Computer VisionAbstract
Accurate detection of cotton pests and diseases is critical for ensuring crop yield. Traditional methods relying on manual expertise suffer from inefficiency and poor robustness. This paper proposes an improved YOLOv8n model (RSS-YOLOv8n) by integrating a Triple Feature Encoding (TFE) module, Scale Sequence Feature Fusion (SSFF) module, Multi-level Feature Fusion (SDI) module, and SENet channel attention mechanism to enhance the detection capability for small targets in complex field environments. Experiments demonstrate that the improved model achieves a mean average precision (mAP) of 88.1% and a frame rate of 37.63 FPS on a self-built dataset (6,358 images), outperforming YOLOv8n by 1.6% mAP and surpassing Faster R-CNN by 8.5% mAP. This method provides an efficient solution for real-time pest and disease detection in agricultural scenarios.
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