###
计算机系统应用:2020,29(6):271-275
←前一篇   |   后一篇→
本文二维码信息
码上扫一扫!
深度迁移学习在紫茎泽兰检测中的应用
(1.云南大学 信息学院, 昆明 650500;2.云南大学 生态学与环境学院, 昆明 650091)
Application of Deep Migration Learning in Detection of Eupatorium Adenophorum
(1.School of Information Science & Engineering, Yunnan University, Kunming 650500, China;2.College of Ecology and Environment, Yunnan University, Kunming 650091, China)
摘要
图/表
参考文献
相似文献
本文已被:浏览 102次   下载 60
投稿时间:2019-10-19    修订日期:2019-11-15
中文摘要: 紫茎泽兰作为中国遭受外来物种入侵的典型例子, 其对生态环境多样性造成严重破坏, 影响农林业经济的发展. 紫茎泽兰检测作为整个防治过程中的初始阶段和监控阶段, 其检测精度会对防治结果造成影响. 针对紫茎泽兰这一类复杂背景叶片图像的目标检测问题, 本文提出一种基于YOLOv3的迁移学习方法来实现紫茎泽兰的检测. 将深度学习模型YOLOv3迁移到紫茎泽兰数据集上, 用K均值算法进行维度聚类确定目标框参数; 在训练过程中改变损失函数中各类损失的权重, 增加模型对数据集的适应性. 实验结果表明, 在紫茎泽兰检测任务中, 平均精度(Average Precision, AP)相较于原YOLOv3提高了17%, 能够满足复杂背景下的紫茎泽兰检测任务.
Abstract:As a typical example of China’s invasion of alien species, Eupatorium adenophorum causes serious damage to the ecological environment and affects the development of agro-forestry economy. Eupatorium adenophorum detection as the initial stage and monitoring stage of prevention and control, its detection accuracy will affect the control results. Aiming at the target detection problem of the complex background leaf image of Eupatorium adenophorum, this study proposes a migration learning method based on YOLOv3 to detect Eupatorium adenophorum. The deep learning model YOLOv3 was migrated to the E. adenophorum data set, and the K-means algorithm was used to perform dimensional clustering to determine the target frame parameters. The weight of various losses is changed in the loss function during training, and the adaptability of the model is increased to the data set. The experimental results show that Average Precision (AP) is 17% higher than that of the original YOLOv3 in the detection task of Eupatorium adenophorum, which can meet the detection task of Eupatorium adenophorum under complex background.
文章编号:7414     中图分类号:    文献标志码:
基金项目:国家自然科学基金(61841112, 31660057)
引用文本:
蒋毅,耿宇鹏,张俊华,王嘉庆,宋颖超.深度迁移学习在紫茎泽兰检测中的应用.计算机系统应用,2020,29(6):271-275
JIANG Yi,GENG Yu-Peng,ZHANG Jun-Hua,WANG Jia-Qing,SONG Ying-Chao.Application of Deep Migration Learning in Detection of Eupatorium Adenophorum.COMPUTER SYSTEMS APPLICATIONS,2020,29(6):271-275

用微信扫一扫

用微信扫一扫