RGB通道增强的草坪杂草识别算法  

RGB channel enhanced lawn weed recognition algorithm

作　　者：化春键 宋一鸣 蒋毅 俞建峰 陈莹[3] HUA Chunjian;SONG Yiming;JIANG Yi;YU Jianfeng;CHEN Ying(School of Mechanical Engineering,Jiangnan University,Wuxi 214122,China;Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment&Technology,Wuxi 214122,China;School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China)

机构地区：[1]江南大学机械工程学院,江苏无锡214122 [2]江苏省食品先进制造装备技术重点实验室,江苏无锡214122 [3]江南大学物联网工程学院,江苏无锡214122

出　　处：《南京农业大学学报》2024年第5期1019-1027,共9页Journal of Nanjing Agricultural University

基　　金：国家自然科学基金项目(62173160)。

摘　　要：[目的]为了实现草坪维护的精准化施药,针对自然环境中杂草与草坪草颜色相近导致杂草难以识别的问题,以YOLOv5s为基础目标检测网络,提出一种突出输入图片颜色信息的草坪杂草检测模型。[方法]对输入图片进行切片,扩展RGB通道数为原来的4倍。对切片后的图片进行全局平均池化和全局最大池化,提取原图片中的颜色信息,对颜色通道进行加权,提高对单色目标检测效果。为了进一步增强网络对于形状相似颜色不同的目标识别效果,改进SPPF模块为SPPFCSPC模块,进一步提高不同草坪环境下的杂草识别精度。[结果]改进后的YOLOv5s网络对于单色目标识别效果较好,对普通草坪杂草的检测精确率达到0.928,召回率为0.970,mAP@0.5为0.943,帧率为104 f·s^(-1),模型内存占用量13.8 MB比使用Focus模块的YOLOv5s检测精度提高4.1百分点,比使用Conv模块的YOLOv5s检测精度提高5.4百分点,在疏林草坪的检测结果mAP@0.5达到0.957。[结论]本文算法可有效识别多种自然环境中的草坪杂草,可为草坪杂草精准化施药提供支持。本文改进模块对颜色的敏感度较好,为与背景颜色相似度较高的单色目标检测提供技术参考。[Objectives]In order to achieve precise herbicide application in lawn maintenance,this study addresses the challenge of weed identification due to the similar colors of weeds and turfgrass in natural environments.Based on the YOLOv5s target detection network,a model highlighting the color information of input images was proposed for lawn weed detection.[Methods]The input images were sliced,and the RGB channels were expanded to four times their original number.The sliced images underwent global average pooling and global max pooling to extract color information from the original images.The color channels were weighted to enhance the detection of monochromatic targets.To further improve the network's ability to recognize targets with similar shapes but different colors,the SPPF module was modified to the SPPFCSPC module,which increased the accuracy of weed identification in various lawn environments.[Results]The improved YOLOv5s network showed a good recognition effect on monochromatic targets,achieving a detection accuracy of 0.928 for common lawn weeds,a recall rate of 0.970,an mAP@0.5 of 0.943,and a frame rate of 104 f·s^(-1),with a model memory usage of 13.8 MB.The detection accuracy improved by 4.1 percentage points compared to YOLOv5s using the Focus module and by 5.4 percentage points compared to YOLOv5s using the Conv module.The detection results in sparse woodland lawns achieved an mAP@0.5 of 0.957.[Conclusions]The algorithm effectively identified various lawn weeds in natural environments and supported precise herbicide application for lawn weeds.The improved module demonstrated good sensitivity to color,providing a technical reference for the detection of monochromatic targets with high background color similarity.

关 键 词：目标检测 YOLOv5s 草坪杂草 精准施药 颜色注意机制 

分 类 号：S126[农业科学—农业基础科学]