基于改进YOLOv4的果园柑橘检测方法研究  

Real-time Citrus Recognition under Orchard Environment by Improved YOLOv4

作　　者：陈文康 陆声链[1,2] 刘冰浩[3] 李帼 刘晓宇[1] 陈明 CHEN Wenkang;LU Shenglian;LIU Binghao;LI Guo;LIU Xiaoyu;CHEN Ming(College of Computer Science and Engineering,Guangxi Normal University,Guilin Guangxi 541004,China;Guangxi Key Lab of Multisource Information Mining and Security,Guangxi Normal University,Guilin Guangxi 541004,China;Guangxi Academy of Specialty Crops/Guangxi Citrus Breeding and Cultivation Engineering Technology Center,Guilin Guangxi 541004,China)

机构地区：[1]广西师范大学计算机科学与工程学院,广西桂林541004 [2]广西师范大学广西多源信息挖掘与安全重点实验室,广西桂林541004 [3]广西特色作物研究院/广西柑橘育种与栽培工程技术中心,广西桂林541004

出　　处：《广西师范大学学报（自然科学版）》2021年第5期134-146,共13页Journal of Guangxi Normal University:Natural Science Edition

基　　金：国家自然科学基金(61762013,62062015);广西科技计划项目(2018AD19339);广西多源信息挖掘与安全重点实验室系统性研究课题基金(20-A-02-02);广西研究生教育创新计划项目(JGY2021037)。

摘　　要：水果的自动检测是自动采摘、果园喷药、采后分拣等农业应用中的关键技术。针对果园环境中柑橘目标小、噪声多、遮挡严重等问题,本文基于YOLOv4算法提出一种改进的适用于果园环境的柑橘快速识别方法。主要改进措施包括:一是在训练阶段利用Canopy算法和K-Means++算法自动选择先验框的数目和大小;二是在YOLOv4网络中每个不同尺度特征的输出层前增加一个调整层,并采用残差网络结构和密集连接网络相结合,同时修改回归框损失函数,以便检测复杂背景下的小柑橘;三是在保证不造成较大检测精度损失的前提下,对网络中不重要的通道和网络层进行剪枝。与目前常用的YOLOv4、MLKP和Cascade R-CNN等3种目标检测算法的对比实验结果表明,本文改进的YOLOv4算法对果园环境下不同生长期柑橘的检测平均准确率达96.04%,平均检测速度为每张图像0.06 s,均优于对比的3种主流目标检测算法。本文提出的方法可为自然条件下果园中柑橘的采摘、产量评估等应用提供技术和方法指导。The automatic detection of fruits is a key technology in agricultural applications such as automatic picking,orchard spraying,and post-harvesting sorting.Aiming at the problems of small citrus targets,many noises,and serious occlusion in the orchard environment,this paper proposes an improved fast identification method for citrus in the orchard environment based on the YOLOv4 algorithm.The main improvement include:one is to use the Canopy algorithm and K-Means++algorithm to automatically select the number and size of the priori boxes in the training phase;the other is to add an adjustment layer before each output layer of different scale features in the YOLOv4 network,where the residual network structure is combined with densely connected network,and the loss function of the regression box is modified to detect small citrus in a complex background;third,on the premise of ensuring that a large amount of detection accuracy is not lost,the unimportant channels and networks in the network Layers are pruned.The experimental results of comparison with the three commonly used target detection algorithms show that the improved YOLOv4 detection method in this paper has better detection results for citrus in different growth periods in the orchard environment,with an average accuracy rate of 96.04%and a real-time detection speed of 0.06 s per image,are better than the above three mainstream target detection algorithms.The method proposed in this paper can provide technical and methodological guidance for citrus harvesting and yield evaluation in orchards under natural conditions.

关 键 词：柑橘识别 小目标检测 深度学习 改进YOLOv4 卷积神经网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]