【摘要】 为准确高效检测糙皮侧耳（Pleurotusostreatus）黄斑病，构建基于YOLOv5s的黄斑病检测模型YOLOv5s-GCE。该模型在YOLOv5s模型基础上引入轻量化GhostNet结构，将坐标注意力（coordinate attention,CA）模块嵌入到YOLOv5s主干网络中，并利用增强交并比（enhancedintersectionoverunion,EIOU）损失函数替换原YOLOv5s网络的完整交并比（complete intersection over union, CIOU）损失函数，利用自建的黄斑病数据集，对YOLOv5s-GCE模型进行消融和对比实验，并将该模型部署在RK3588S人工智能开发板上进行测试。结果表明：相比于原始YOLOv5s模型，YOLOv5s-GCE模型的平均精度均值（mean averageprecision,mAP）为92.7%（提高2.7%），复杂度显著降低，参数量、权重大小和浮点运算量（giga floating-pointoperationspersecond,GFLOPs）分别降低44.7%、43.4%和47.2%; YOLOv5s-GCE模型的整体性能优于SSD、YOLOv7、YOLOv8n和Faster R-CNN典型的目标检测模型。部署在RK3588S开发板上的YOLOv5s-GCE模型检测速度可达每秒30.49帧，mAP值为90.2%，可以满足糙皮侧耳黄斑病实时检测需求，研究结果为后续研发食用菌病害智能检测装置提供参考。更多还原

【Abstract】 For accurate and efficient detection of yellow spot disease in Pleurotus ostreatus, a model named YOLOv5s-GCE was developed based on the YOLOv5s model. YOLOv5s-GCE integrated a lightweight GhostNet structure, embedded a coordinate attention(CA) module into the YOLOv5s backbone, and substituted the original CIOU loss function of YOLOv5s with the enhanced intersection over union(EIOU)loss function. Using a self-built yellow spot disease dataset, ablation and comparison experiments were conducted on YOLOv5s-GCE. Subsequently, the model was deployed on an RK3588S AI development board for validation. The results showed that YOLOv5s-GCE outperformed YOLOv5s in terms of mean average precision(mAP)(92.7%, 2.7% increase over the baseline), complexity(significantly reduced), parameter count(decreased by 44.7%), model size(decreased by 43.4%), and computational cost(decreased by 47.2% in giga floating-point operations per second, GFLOPs). The overall performance of YOLOv5s-GCE was superior to other typical object detection models, such as SSD, YOLOv7, YOLOv8n, and Faster R-CNN. The detection speed of YOLOv5s-GCE deployed on RK3588S development board was 30.49 frames per second with an mAP value of 90.2%, which satisfied requirements of real-time detection of P. ostreatus yellow spot disease.The results provided a reference for subsequent development of intelligent devices for detecting pathogenic diseases in edible fungi.更多还原