基于改进YOLOv8的轻量化小麦病害检测方法

为提高小麦病害检测精度，实现将模型方便快速部署到移动端，该研究提出了一种基于改进YOLOv8的轻量化小麦病害检测方法。首先，使用PP-LCNet模型替换YOLOv8网络结构的骨干网络，并在骨干网络层引入深度可分离卷积（depthwise separable convolution, DepthSepConv）结构，减少模型参数量，提升模型检测性能；其次，在颈部网络部分添加全局注意力机制（global attention mechanism, GAM）模块，强化特征中语义信息和位置信息，提高模型特征融合能力；然后，引入轻量级通用上采样内容感知重组（content-aware reassembly of features,CARAFE）模块，提高模型对重要特征的提取能力；最后，使用Wise-IoU(weighted interpolation of sequential evidence for intersection over union)边界损失函数代替原损失函数，提升网络边界框回归性能和对小目标病害的检测效果。试验结果表明，对于大田环境下所采集的小麦病害数据集，改进后模型的参数量及...

Method for the lightweight detection of wheat disease using improved YOLOv8

Wheat diseases have posed a severe threat to the grain quality and yield in wheat production. It is highly required for the automatic, rapid, and accurate identification of wheat diseases on equipment with limited resources, particularly for timely preventive measures. In this study, a lightweight detection was proposed for wheat diseases using an improved YOLOv8n, termed PGCW-YOLOv8. Firstly, a lightweight CPU network (PP-LCNet) was introduced to replace the backbone network of the YOLOv8 structure, in order to reduce the large model weight files. Depthwise separable convolution (DepthSepConv) structure was introduced into the Backbone layer to reduce the parameter quantity, thereby reducing the size of weight files for better detection performance. Secondly, a global attention mechanism (GAM) module was added to the Neck section, in order to enhance the feature extraction and fusion of the network. As such, the model was improved to better focus on the small features of the target disease. The higher detection accuracy of minor lesions was achieved to better understand the important information in the image through a global attention mechanism, thereby accurately identifying the diseases under complex background and lighting conditions. Thirdly, a lightweight content-aware reassembly of features (CARAFE) module was introduced to aggregate the context information within a larger receptive field, in order to improve the detection accuracy of the model. The detailed image information was then effectively preserved using upsampling and downsampling operations. Finally, the Wise-IoU boundary loss function was used instead of the original loss function, in order to enhance the bounding box regression performance of the network model. The position and size of disease features were better learnt to improve the detection of small target diseases. Experimental results show that the improved PGCW-YOLOv8 model reduced the computational complexity (GFLOPs), parameters, and model size by 13.6%, 12.5%, and 11.3%, respectively, in the wheat disease datasets collected in field environments, compared with the original YOLOv8n baseline model. Meanwhile, the precision, mean average precision (mAP), and frames per second (FPS) of the improved model increased by 4.5, 1.9 percentage points, and 23.1%, respectively, compared with the original. A comparison was made under the same experimental conditions with the mainstream deep learning models, such as Faster R-CNN, YOLOv5s, YOLOv7, Yolov7-tiny, YOLOXs, and Edge-YOLO. The improved PGCW-YOLOv8 model shared the highest precision and mAP values, while the lowest computational complexity (GFLOPs), parameters, and model size. Three datasets of comparative experiments indicate that the lightweight PGCW-YOLOv8 network model outperformed the original YOLOv8 and YOLOv5 models, especially in the accurate detection of the wheat disease features under complex background and multi-target situations. The finding can provide a strong reference for the intelligent detection of wheat diseases in real time, particularly for rapid detection applications, such as deployment on unmanned aerial vehicles and mobile terminal equipment.