Authors

Abstract

To overcome YOLOv11’s limitations in complex field environments, this paper proposed SDD-YOLOv11n, a lightweight real-time detector for soybean diseases. The model reconstructed the backbone using GhostConv to minimize redundancy and integrates a C3k2_Star module to enhance small lesion detection against background noise. Additionally, a Detect Efficient (DE) head further compressed the architecture. Experimental results verified the model's efficiency, achieving a parameter count of 1.88 M and a weight size of 3.9 MB—reductions of 27.3% and 25% compared to YOLOv11n, respectively. Furthermore, the model maintained high detection performance with a Mean Average Precision (mAP50) of 75.6% and an F1-score of 69.3%, demonstrating its effectiveness in balancing architectural efficiency and accuracy in complex field environments.

Abstract in Chinese

针对YOLOv11在复杂田间环境下存在的局限性，本文提出了一种轻量化的大豆病害实时检测模型——SDD-YOLOv11n。该模型利用GhostConv重构主干网络以最大限度地降低特征冗余，并引入C3k2_Star模块，旨在抑制背景噪声干扰的同时增强对微小病斑的检测能力。此外，设计了Detect Efficient (DE)检测头以进一步压缩模型架构。试验结果验证了该模型的高效性，其参数量仅为1.88 M，权重文件大小为3.9 MB，较YOLOv11n分别降低了27.3%和25%。同时，该模型在mAP50和F1-Score上分别达到了75.6%和69.3%，证明了其在检测精度和架构效率之间的良好平衡，能够有效应对复杂田间环境下的检测任务。