草业学报2025，Vol.34Issue(1)：41-54,14.DOI:10.11686/cyxb2024060
基于YOLOv5和改进DeeplabV3+的青藏高原植被提取算法
Vegetation extraction algorithm for the Tibetan Plateau based on YOLOv5 and improved DeeplabV3+
摘要
Abstract
Vegetation coverage on the Qinghai-Tibet Plateau is a crucial metric for ecological studies and environmental monitoring.Traditional methods to detect vegetation coverage are effective in regions with simple terrains and concentrated vegetation.However,in complex terrains,issues such as high costs,restricted survey areas,and extended time intervals reduce the accuracy of the results obtained using such traditional methods.In recent years,rapid advancements in computer vision and deep learning have created new opportunities for precise vegetation extraction in the complex terrains of the Qinghai-Tibet Plateau.Here,we introduce a two-stage vegetation extraction algorithm that integrates YOLOv5 and an improved DeeplabV3+.The algorithm utilizes a vegetation detection model based on YOLOv5 to minimize background interference during the second stage of vegetation segmentation;and a newly designed DeeplabV3+semantic segmentation model for accurate vegetation segmentation and extraction.The improved model incorporates the lightweight backbone network MobileNetV2,optimizes the dilated convolution parameters of the ASPP module,and integrates EMA and CloAttention mechanisms.The experimental results on the unmanned aerial vehicle dataset of the Qinghai-Tibet Plateau demonstrate that the algorithm attains an intersection over union(IoU)of 90.40% and a pixel accuracy(PA)of 96.32%,significantly outperforming other current technologies and greatly reducing the model's parameters.Under various environmental conditions,the algorithm exhibits high-precision capabilities for vegetation extraction,offering effective technical support for the rapid and precise measurement of vegetation cover on the Qinghai-Tibet Plateau.关键词
青藏高原/植被提取/深度学习/YOLOv5/DeeplabV3+Key words
Tibetan Plateau/vegetation extraction/deep learning/YOLOv5/DeeplabV3+引用本文复制引用
闫储淇,黄建强..基于YOLOv5和改进DeeplabV3+的青藏高原植被提取算法[J].草业学报,2025,34(1):41-54,14.基金项目
青海省重点研发计划:地球系统模式公共软件平台在青藏高原气候诊断评估的应用与推广(2023-QY-208)资助. （2023-QY-208）
