Pine wilt disease (PWD) poses a significant threat to forests due to its high infectivity and lethality. The absence of an effective treatment underscores the importance of timely detection and isolation of infected trees for effective prevention and control. While deep learning techniques combined unmanned aerial vehicle (UAV) remote sensing images offer promise for accurate identification of diseased pine trees in their natural environments, they often demand extensive prior professional knowledge and struggle with efficiency. This paper proposes a detection model YOLOv5L-s-SimAM-ASFF, which achieves remarkable precision, maintains a lightweight structure, and facilitates real-time detection of diseased pine trees in UAV RGB images under natural conditions. This is achieved through the integration of the ShuffleNetV2 network, a simple parameter-free attention module known as SimAM, and adaptively spatial feature fusion (ASFF). The model boasts a mean average precision (mAP) of 95.64% and a recall rate of 91.28% in detecting pine wilt diseased trees, while operating at an impressive 95.70 frames per second (FPS). Furthermore, it significantly reduces model size and parameter count compared to the original YOLOv5-Lite. These findings indicate that the proposed model YOLOv5L-s-SimAM-ASFF is most suitable for real-time, high-accuracy, and lightweight detection of PWD-infected trees. This capability is crucial for precise localization and quantification of infected trees, thereby providing valuable guidance for effective management and eradication efforts.

中文翻译：

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基于改进YOLOv5的松材线虫病检测算法

松材线虫病（PWD）因其高传染性和致死率而对森林构成重大威胁。缺乏有效的治疗方法凸显了及时发现和隔离受感染树木以进行有效预防和控制的重要性。虽然深度学习技术与无人机 (UAV) 遥感图像相结合，有望准确识别自然环境中的患病松树，但它们通常需要广泛的专业知识，并且效率低下。本文提出了一种检测模型YOLOv5L-s-SimAM-ASFF，该模型实现了卓越的精度，保持了轻量级结构，并有利于在自然条件下实时检测无人机RGB图像中的病松树。这是通过集成 ShuffleNetV2 网络、称为 SimAM 的简单无参数注意力模块和自适应空间特征融合 (ASFF) 来实现的。该模型在检测松材线虫病树木方面的平均精度 (mAP) 为 95.64%，召回率为 91.28%，同时运行速度高达 95.70 帧/秒 (FPS)。此外，与原始 YOLOv5-Lite 相比，它显着减少了模型大小和参数数量。这些结果表明，所提出的模型 YOLOv5L-s-SimAM-ASFF 最适合实时、高精度、轻量级检测受 PWD 感染的树木。这种能力对于受感染树木的精确定位和量化至关重要，从而为有效管理和根除工作提供有价值的指导。