基于特征交互的样本不均衡的玉米病害检测方法

    A maize disease detection method based on feature interaction under imbalanced sample condition

    • 摘要:
      目的 解决复杂环境中玉米叶片病害数据样本不均衡、检测精度低的问题。
      方法 设计一种改进的目标检测网络 SF_YOLOv5。首先,在YOLOv5的多尺度金字塔结构基础上,设计一种新的空间−特征金字塔结构(SPD-FPN),增强网络对小目标病害特征在高分辨率层次上的识别能力,保留大目标在低分辨率层次上的信息,整体提升网络的检测精度和鲁棒性。其次,引入 Focal Loss 损失函数,通过增加难分类样本的权重,减少易分类样本的影响,确保模型能够更关注不平衡数据集中易被忽略的少量样本。此外,将迁移学习的思想应用于 SF_YOLOv5的设计中,将预训练得到的 YOLOv5模型参数迁移到改进的 SF_YOLOv5网络上进行训练,利用已有的大规模数据集知识,提升网络对玉米病害检测的泛化能力。
      结果 在构建的玉米病害数据集上验证SF_YOLOv5模型的均值检测精度(mAP)达 93.3%,召回率为 89.6%,相比原始 YOLOv5 模型有显著提升。且模型体积较小,易部署于移动端设备。
      结论 改进后的网络检测样本不均衡的玉米叶片病害效果优于原模型,可用于农田场景下样本不均衡的玉米病害的智能诊断,为农业领域实时监测玉米病害提供理论基础。

       

      Abstract:
      Objective To address the issues of imbalanced data samples and low detection accuracy in maize leaf disease detection under complex environments.
      Method An improved object detection network, SF_YOLOv5 was proposed. First, based on the multi-scale pyramid structure of YOLOv5, a novel spatial-feature pyramid structure (SPD-FPN) was designed to enhance the network’s ability to recognize small-target disease features at high-resolution levels while retaining large-target information at low-resolution levels, thereby improving overall detection accuracy and robustness. Second, the Focal Loss function was introduced to increase the weight of hard-to-classify samples and reduce the influence of easily classified samples, ensuring that the model focused more on the minority samples often overlooked in imbalanced datasets. Additionally, transfer learning was applied to the design of SF_YOLOv5, where pre-trained YOLOv5 model parameters were transferred to the improved SF_YOLOv5 network for training. This leveraged knowledge from large-scale datasets to enhance the model’s generalization capability for maize disease detection.
      Result Experimental validation on the constructed maize disease dataset showed that SF_YOLOv5 achieved a mean average precision (mAP) of 93.3% and a recall of 89.6%, significantly outperforming the original YOLOv5 model. And the model was small in size, and had been deployed in mobile devices.
      Conclusion The results demonstrate that the improved network performs better than the original model in detecting maize leaf diseases under imbalanced sample conditions. This approach can be applied to intelligent diagnosis of maize diseases in farmland scenarios with imbalanced data, providing a theoretical foundation for real-time maize disease detection in the agricultural sector.

       

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