基于改进YOLOv5的智能除草机器人蔬菜苗田杂草检测研究  

Research on weed detection in vegetable seedling fields based on the improved YOLOv5 intelligent weeding robot

作　　者：张伟康 孙浩 陈鑫凯 李叙兵 姚立纲[1] 东辉 ZHANG Wei-kang;SUN Hao;CHEN Xin-kai;LI Xu-bing;YAO Li-gang;DONG Hui(School of Mechanical Engineering and Automation,Fuzhou University,Fuzhou Fujian 350108,China)

机构地区：[1]福州大学机械工程及自动化学院,福建福州350108

出　　处：《图学学报》2023年第2期346-356,共11页Journal of Graphics

基　　金：国家自然科学基金项目(62173093);福建省自然科学基金项目(2020J01456)。

摘　　要：杂草精准检测是自动化除草装备的关键技术。针对田间杂草分布复杂和种类繁多导致的检测复杂度高和鲁棒性差等问题,基于自研移动机器人平台,提出一种改进YOLOv5算法和图像处理的蔬菜苗田杂草检测方法。通过识别蔬菜间接检测杂草的方式降低杂草检测复杂度,进而提高检测精度和鲁棒性。在YOLOv5目标检测算法主干特征提取网络中引入卷积块注意力模块(CBAM)提高网络对蔬菜目标的关注度,加入Transformer模块增强模型对全局信息的捕捉能力。结果表明,改进YOLOv5算法对蔬菜目标的平均检测准确率可达95.7%,与Faster R-CNN,SSD,EfficientDet,RetinaNet,YOLOv3,YOLOv4和YOLOv5算法相比,分别提高了5.8%,6.9%,10.3%,13.1%,9.0%,5.2%和3.2%。算法单幅图像平均检测时间11 ms,具有较好的实时性。采用改进YOLOv5算法检测蔬菜,将蔬菜边框之外绿色植物定义为杂草,超绿特征(ExG)结合OTSU阈值分割法将杂草与土壤背景分割,最后标记杂草连通域输出杂草质心和检测框。本研究方法可为农业自动化精准除草提供借鉴。Accurate detection of weeds is a key technology for developing automated weeding equipment.To address the problems of high detection complexity and poor robustness resulting from the complex distribution and variety of weeds,we proposed a weed detection approach for vegetable seedling based on the improved YOLOv5 algorithm and image processing,implemented on a self-developed mobile robot platform.The weed detection complexity was reduced by indirectly detecting weeds through identifying vegetables,thus improving the detection accuracy and robustness.The convolutional block attention module(CBAM)attention module was added to the backbone feature extraction network of the YOLOv5 object detection algorithm to enhance the focus of the network on vegetable targets,and the Transformer module was added to enhance the global information capture capability.The results showed that the average detection accuracy of the improved YOLOv5 algorithm for vegetable targets could reach 95.7%,which was increased by 5.8%,6.9%,10.3%,13.1%,9.0%,5.2%,and 3.2%compared with Faster R-CNN,SSD,EfficientDet,RetinaNet,YOLOv3,YOLOv4,and YOLOv5,respectively.The average detection time of the algorithm for a single run was 11 ms,indicating good real-time performance.The method defined green plants outside the vegetable border as weeds,and combined the extreme green(ExG)with the OTSU threshold segmentation method to segment weeds from the soil background.Finally,the weed connectivity domain was marked,followed by outputting the weed plasmids and detection frames.The proposed method could provide a technical reference for automated precision weeding in agriculture.

关 键 词：除草机器人 杂草检测 蔬菜识别 YOLOv5 注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]