一种基于改进YOLOv4算法的茶树芽叶采摘点识别及定位方法  

Identification and Localization Method of Tea Bud Leaf Picking Point Based on Improved YOLOv4 Algorithm

作　　者：徐凤如 张昆明 张武[1] 王瑞卿 汪涛 万盛明 刘波[1] 饶元[1] XU Fengru;ZHANG Kunming;ZHANG Wu;WANG Ruiqing;WANG Tao;WAN Shengming;LIU Bo;RAO Yuan(School of Information&Computer,Anhui Agricultural University,Hefei,Anhui 230036,China)

机构地区：[1]安徽农业大学信息与计算机学院,安徽合肥230036

出　　处：《复旦学报（自然科学版）》2022年第4期460-471,共12页Journal of Fudan University：Natural Science

基　　金：安徽省重点研究和开发计划项目(202204c06020022,201904a06020056,202104a06020012);智慧农业技术与装备安徽省重点实验室开放基金(APKLSATE2019X001);安徽农业大学大学生创新创业训练计划项目(S202110364053)。

摘　　要：针对复杂环境下农业采茶机器人无法快速、准确地识别与定位茶树芽叶采摘点的问题,本文采用改进型YOLOv4-Dense算法和OpenCV图像处理方法,对茶树芽叶采摘点的定位问题进行研究。首先,基于YOLOv4算法,将CSPDarkNet53主干特征提取网络中的ResNet单元替换为DenseNet单元,使用改进后的算法模型对采集的茶树数据集进行芽叶目标检测;其次,运用OpenCV图像处理方法进行RGB-HSV颜色转换获取芽叶的轮廓,并基于形态学算法定位采摘点的位置;最后,开展采摘点定位方法的对比实验,分别与矩函数法、最小外接矩形中心点法的定位结果进行对比。实验结果表明:1)改进型YOLOv4-Dense算法在芽叶目标检测上的精确率为91.83%,召回率为68.84%,AP值为86.55%,F1分数为0.79;与YOLOv4模型的精确率、召回率、AP值、F1分数相比分别提升了2.21%,2.00%;2.05%,0.02;与YOLO v3模型相比它们分别提升了,5.56%,15.26%;9.13%,0.13;2)针对自然条件下的茶树芽叶,采用OpenCV图像处理方法定位采摘点的精确率为80.8%,召回率为83.2%,与矩函数法、最小外接矩形中心点法相比,分别提升了3.5%,7.1%;1.4%,6.1%。实验数据说明本研究方法对于芽叶采摘点的准确识别与定位具有一定的借鉴意义。In view of the difficulty in identifying and locating the picking point of tea bud leaves in complex environment with the agricultural tea picking robot quickly and accurately,the improved YOLOv4-Dense algorithm and the OpenCV image processing method are used to study the positioning of tea bud leaf picking points.Firstly,based on the YOLOv4 algorithm,the ResNet unit in the CSPDarknet53 trunk feature extraction network is replaced with the DenseNet unit,then the improved algorithm model was used to detect the tea bud leaves of the collected tea plant dataset.Secondly,the color channel conversion of RGB to HSV is performed to obtain the outline of the bud leaves by using OpenCV,then the location of picking points is located by OpenCV morphological processing.Finally,conduct comparative experiments on picking point positioning methods and compare with the positioning results of the moment function method and the smallest external rectangular center point method.The results show that:1)The precision of the improved YOLOv4-Dense algorithm in the detection of tea bud leaves was 91.83%,the recall rate was 68.84%,the AP was 86.55%,and the F1 score was 0.79;Compared with the YOLOv4 model,the precision,recall,AP value,and F1 score improved by 2.21%,2.00%;2.05%,and 0.02;Compared with the YOLO v3 model they improved by 5.56%,15.26%;9.13%and 0.13;2)For tea bud leaves under natural conditions,the accuracy of locating the picking point using OpenCV image processing method was 80.8%,and the recall rate was 83.2%,compared with the moment function method and the minimum external rectangular center point method,it is increased by 3.5%,7.1%;1.4%,6.1%,indicating that this method has certain reference significance for the accurate identification and positioning of tea bud leaves picking points.

关 键 词：图像处理 茶树芽叶 采摘点 深度学习 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]