改进YOLOv11检测棉花病害小病斑，提升精度与实时性。
C2PSA-Enhanced YOLOv11 Architecture: A Novel Approach for Small Target Detection in Cotton Disease Diagnosis
- 引入C2PSA模块增强小目标特征提取能力
- mAP50达0.820，小病斑漏检率降至35%
- 适合田间实时监测，支持移动端部署
本研究针对棉花病害检测中的三大挑战：早期小病斑检测精度低（<5mm²病斑漏检率达35%）、野外环境性能下降（准确率降低25%）、多病种场景误判率高（达34.7%），提出优化YOLOv11的深度学习框架。通过引入C2PSA模块增强小目标特征提取，采用动态类别加权缓解样本不平衡，结合Mosaic-MixUp数据增强策略提升泛化能力。在包含4,078张图像的数据集上测试显示：mAP50达到0.820（提升8.0%），mAP50-95达0.705（提升10.5%），推理速度达158 FPS。移动端部署系统可实现农田实时病害监测与精准施药。
原文摘要 · Abstract (English)
This study presents a deep learning-based optimization of YOLOv11 for cotton disease detection, developing an intelligent monitoring system. Three key challenges are addressed: (1) low precision in early spot detection (35% leakage rate for sub-5mm2 spots), (2) performance degradation in field conditions (25% accuracy drop), and (3) high error rates (34.7%) in multi-disease scenarios. The proposed solutions include: C2PSA module for enhanced small-target feature extraction; Dynamic category weighting to handle sample imbalance; Improved data augmentation via Mosaic-MixUp scaling. Experimental results on a 4,078-image dataset show: mAP50: 0.820 (+8.0% improvement); mAP50-95: 0.705 (+10.5% improvement); Inference speed: 158 FPS. The mobile-deployed system enables real-time disease monitoring and precision treatment in agricultural applications.
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