轻量化YOLO模型在农作物微小病虫害检测中的应用研究
Research on the application of lightweight YOLO model in detection of small crop diseases and pests
杨巧梅 1崔婷婷 1袁永榜 2罗桦3
作者信息
- 1. 重庆对外经贸学院,重庆市,401520
- 2. 中煤科工集团重庆研究院有限公司,重庆市,400039
- 3. 重庆三峡学院,重庆市,404020
- 折叠
摘要
针对农作物病虫害检测中早期微小病虫害变化目标识别准确率不高的问题,提出一种轻量化农作物微小病虫害检测算法YOLO-MobileNet-CBAM.该算法采用MobileNetV3轻量级卷积模块替换YOLOv5s的主干提取网络来降低参数运算量,并引入CBAM注意力机制从通道和空间两个维度对重要的特征提取进行强化,有效增强小目标的聚焦准确度.卷积模块中用H-SiLU代替原模型的SiLU激活函数提高训练速度,避免梯度消失问题.通过将SIoU函数代替原模型中的GIoU函数计算预测框回归损失,将形状损失计算在内,进一步提高小目标定位精度.通过特征金字塔输出4个不同尺度的检测头识别大面积病害、微小病害及虫害目标,增加微小目标的检测精度.结果表明,YOLO-MobileNet-CBAM在微小病虫害目标检测任务中精确率达92.38%,召回率达90.24%,平均精度大于90%.实现模型轻量化,同时有效提高检测精确度,为手持式终端检测应用提供技术支持.
Abstract
In response to the problem of insufficient accuracy in early small change target recognition in crop pest detection,a lightweight plant pest detection algorithm YOLO-MobileNet-CBAM is proposed.This algorithm replaces the backbone extraction network of YOLOv5s with a lightweight convolutional module of MobileNetV3 to reduce parameter computation,and introduces CBAM attention mechanism to strengthen important feature extraction from both channel and spatial dimensions,effectively enhancing the detection accuracy of small targets.It improves training speed and avoids gradient vanishing problems by replacing the original model's SiLU activation function with H-SiLU in the convolutional module.The prediction box regression loss function utilizes the SIoU function instead of the GIoU function in the original model,accounting for shape loss to further improve accuracy of small target localization.Finally,four detection heads with varying scales are output via the feature pyramid to identify large-scale diseases,small diseases and pest targets,thereby enhancing the detection accuracy of small targets.The results show that YOLO-MobileNet-CBAM achieves an accuracy rate of 92.38%,a recall rate of 90.24%,and an average accuracy of over 90%in detecting small pests and diseases.It achieves lightweight model design while effectively improving detection accuracy,and provides technical support for handheld terminal detection applications.
关键词
农作物/微小病虫害检测/轻量化模型/YOLO-MobileNet-CBAMKey words
crops/small diseases and pests/lightweight model/YOLO-MobileNet-CBAM引用本文复制引用
基金项目
重庆市教育委员会科学技术研究项目(KJQN202101233)
出版年
2024
国家科技期刊平台
NSTL
万方数据