基于改进YOLOv4的棉花检测算法  

Cotton Detection Algorithm Based on Improved YOLOv4

作　　者：刘正波 鲍义东 孟庆伟 LIU Zheng-Bo;BAO Yi-Dong;MENG Qing-Wei(Guizhou Aerospace Intelligent Agriculture Co.Ltd.,Guiyang 550081,China)

机构地区：[1]贵州航天智慧农业有限公司,贵阳550081

出　　处：《计算机系统应用》2021年第8期164-170,共7页Computer Systems & Applications

摘　　要：为提高自动化采棉机械的采摘效率和智能化水平,避免误采摘、漏采摘,采用以复杂背景下实现单个棉花检测为目标,提出一种改进的YOLOv4目标检测算法.使用K-means算法进行聚类锚框尺寸的筛选,得到适合棉花数据集的精细化锚框尺寸.同时在YOLOv4算法中引入注意力机制,在其网络结构中添加SENet(Squeeze-and-Excitation Networks)模块.在模型训练时,首先在公开数据集上训练取得预训练权重,在预训练模型上使用棉花数据集微调参数,并使用数据增强方式扩充原始数据集,在预训练模型上再次训练.实验结果表明,本文提出的YOLOv4改进算法,能够很好的实现田间环境下的棉花检测.To improve the efficiency and intelligence of automatic cotton-picking machines and avoid false and missed picking,we propose an improved YOLOv4 target detection algorithm to detect single cotton in complex backgrounds.The K-means algorithm is used to screen the size of the clustering anchor frame and obtain the refined size suitable for the cotton data set.The attention mechanism is also introduced to the YOLOv4 algorithm,and the Squeeze-and-Excitation Networks(SENet)module is located in the network structure.During model training,the weights of pre-training are obtained by training on an open data set,and fine-tuning parameters of the cotton data set are applied to the pre-training model.Furthermore,the original data set is expanded through data enhancement and the pre-training model has been trained again.Experimental results show that the improved YOLOv4 algorithm proposed in this study can effectively realize cotton detection in the field environment.

关 键 词：棉花采摘 检测 YOLOv4 聚类 SENet 模型 数据增强 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] S562[自动化与计算机技术—计算机科学与技术]