MSH-YOLOv8:融合尺度重建的蘑菇小目标检测方法  

MSH-YOLOv8:Mushroom Small Object Detection Method with Scale Reconstruction and Fusion

作　　者：叶大鹏[1,2] 景均 张之得 李辉煌 吴昊宇 谢立敏 YE Dapeng;JING Jun;ZHANG Zhide;LI Huihuang;WU Haoyu;XIE Limin(College of Mechanical and Electrical Engineering,Fujian Agriculture and Forestry University,Fuzhou 350002,China;Fujian Key Laboratory of Agricultural Information Sensoring Technology,Fuzhou 350002,China;School of Future Technology,Haixia Institute of Science and Technology,Fujian Agriculture and Forestry University,Fuzhou 350002,China)

机构地区：[1]福建农林大学机电工程学院,福建福州350002 [2]福建省农业信息感知技术重点实验室,福建福州350002 [3]福建农林大学未来技术学院,福建福州350002

出　　处：《智慧农业（中英文）》2024年第5期139-152,共14页Smart Agriculture

基　　金：福建省林业科技项目(2023FKJ01)。

摘　　要：[目的/意义]为了解决图像尺寸变化和目标尺度变换共存对小目标检测精度的影响问题,本研究提出了一种新的检测模型:Multi-Strategy Handling YOLOv8(MSH-YOLOv8)。[方法]该模型在YOLOv8的基础上增加一个检测头,以提高小尺度目标敏感度;引入Swin Transformer的检测结构到头部网络,以减少计算冗余;引入包含可变形卷积的C2f_Deformable Convolutionv4(C2f_DCNv4)结构和Swin Transformer编码器结构重构YOLOv8主干网络,优化并增强其特征传递和提取能力,提高小目标敏感度;采用基于规范化的注意力模块(Normalizationbased Attention Module,NAM)优化网络检测速度和准确性;用Wise-Intersection over Union Loss(WIoU)代替原损失函数,以提高训练效果和收敛速度;在后处理阶段应用分辨率动态训练、多尺度测试、软非极大值抑制算法(Soft-Non-Maximum Suppression,Soft-NMS)、加权边界框融合算法(Weighted Boxes Fusion,WBF)等方法,提高尺度变化下小目标检测效果。以蘑菇为研究对象,在开放数据集Fungi上开展实验。[结果和讨论]MSH-YOLOv8的平均正确率(Average Precision50,AP50)和AP@50-95分别达到了98.49%和75.29%,其中小目标检测指标值APs达39.73%。相较于主流模型YOLOv8,三项指标分别提高了2.34%,4.06%和8.55%;相较于优秀模型Transformer Prediction Heads-YOLOv5(TPH-YOLOv5),三项指标分别提高了2.14%,2.76%和6.89%。[结论]本研究提出的MSH-YOLOv8改进方法可在图像尺寸变化与目标尺度变化条件下有效提高小目标的检测效果。[Objective]Traditional object detection algorithms applied in the agricultural field,such as those used for crop growth monitoring and harvesting,often suffer from insufficient accuracy.This is particularly problematic for small crops like mushrooms,where recognition and detection are more challenging.The introduction of small object detection technology promises to address these issues,potentially enhancing the precision,efficiency,and economic benefits of agricultural production management.However,achieving high accuracy in small object detection has remained a significant challenge,especially when dealing with varying image sizes and target scales.Although the YOLO series models excel in speed and large object detection,they still have shortcomings in small object detection.To address the issue of maintaining high accuracy amid changes in image size and target scale,a novel detection model,Multi-Strategy Handling YOLOv8(MSH-YOLOv8),was proposed.[Methods]The proposed MSH-YOLOv8 model builds upon YOLOv8 by incorporating several key enhancements aimed at improving sensitivity to small-scale targets and overall detection performance.Firstly,an additional detection head was added to increase the model's sensitivity to small objects.To address computational redundancy and improve feature extraction,the Swin Transformer detection structure was introduced into the input module of the head network,creating what was termed the"Swin Head(SH)".Moreover,the model integrated the C2f_Deformable convolutionv4(C2f_DCNv4)structure,which included deformable convolutions,and the Swin Transformer encoder structure,termed"Swinstage",to reconstruct the YOLOv8 backbone network.This optimization enhanced feature propagation and extraction capabilities,increasing the network's ability to handle targets with significant scale variations.Additionally,the normalization-based attention module(NAM)was employed to improve performance without compromising detection speed or computational complexity.To further enhance training efficacy and con

关 键 词：图像尺寸 小目标检测 特征提取 多尺度检测 模型集成 

分 类 号：S24[农业科学—农业电气化与自动化] TP391[农业科学—农业工程]