基于改进YOLOv8n的真实场景下草莓病害检测方法

摘要：

针对实际种植环境下草莓病害目标检测中，存在背景复杂、检测精度低等主要问题，提出一种改进YOLOv8n的草莓病害检测算法YOLOv8n-SD。搜集并处理真实场景下草莓叶、花、果的常见病害图像以构建试验数据集。在YOLOv8n模型的基础上对其进行优化改进，利用多尺度并行计算与补丁感知注意力对主卷积模块进行重构，提出C2f-PPA模块，有效融合多尺度特征信息，提高模型的特征捕获能力。引入ADown模块，减少下采样过程中的信息损失，提高模型的推理速度和鲁棒性。提出一种任务对齐的共享动态检测头（Task-aligned Dynamic Head，TDyH），增强定位分支和分类分支之间的信息交互，降低模型参数的同时，提高检测精度和准确性。根据试验结果，改进后的YOLOv8n-SD模型的检测精度达到83.7%，相较于原YOLOv8n提高3.3%，mAP@0.5与mAP@0.5∶0.95分别达到76.9%和59.9%，分别提升1.6%和2.3%。改进后的算法能精确识别草莓生长各阶段的常见病害，并满足边缘设备的轻量化需求和实时检测需求。

关键词: 草莓病害, 目标检测, YOLOv8n, ADown, 轻量化, 实时检测

Abstract:

Addressing the primary issues of complex backgrounds and low detection accuracy in strawberry disease target detection under practical farming conditions, an improved YOLOv8n strawberry disease detection algorithm as YOLOV8N-SD is proposed. Images of common strawberry leaf, flower, and fruit diseases under real-world scenarios are collected and processed to construct an experimental dataset. Optimizations and improvements are made to the YOLOv8n model. The primary convolutional module is reconstructed by using multi-scale parallel computing and patch-perceptive attention, introducing the C2f-PPA module. This effectively integrates multi-scale feature information, enhancing the model's feature capturing capability. Additionally, the ADown module is incorporated to reduce information loss during downsampling, thereby improving the model's inference speed and robustness. A Task-aligned Dynamic Head (TDyH) is proposed to strengthen information exchange between the localization and classification branches. This reduces model parameters while simultaneously enhancing detection precision and accuracy. According to experimental results, the improved YOLOv8n-SD model achieves a detection accuracy of 83.7%, representing a 3.3% increase over the original YOLOv8n. Its mAP@0.5 and mAP@0.5∶0.95 scores reach 76.9% and 59.9% respectively, marking improvements of 1.6% and 2.3% compared to the baseline. This enhanced algorithm not only accurately identifies common diseases at various stages of strawberry growth but also meets the lightweight and real-time detection requirements of edge devices.

Key words: strawberry diseases, target detection, YOLOv8, ADown, lightweight, real-time detection

中图分类号: 

S436.68

TP391.4

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