现代信息科技2024，Vol.8Issue(4)：132-135,141,5.DOI:10.19850/j.cnki.2096-4706.2024.04.028
基于改进DeepLabv3+的道路分割算法
Road Segmentation Algorithm Based on Improved DeepLabv3+
摘要
Abstract
The use of Deep Learning-based remote sensing image segmentation technology is becoming increasingly widespread.In response to the problems of large parameter quantities and poor results in extracting details in existing algorithms,a road image segmentation method based on improved DeepLabv3+is proposed.Introducing the lightweight network MobileNetV2 into an improved pooling pyramid model to extract mid-order feature maps,which enhance the correlation between different receptive fields.A multi-scale concatenation fusion method is adopted to generate high-order feature maps,while introducing attention mechanisms to further enhance the extraction effect of image features.The experimental results show that the proposed method improves mIoU by 5％compared to the DeepLabv3+model,effectively enhancing the segmentation accuracy of remote sensing images.关键词
语义分割/遥感影像/道路提取/注意力机制/DeepLabv3+Key words
semantic segmentation/remote sensing image/road extraction/Attention Mechanism/DeepLabv3+分类
信息技术与安全科学引用本文复制引用
葛振强..基于改进DeepLabv3+的道路分割算法[J].现代信息科技,2024,8(4):132-135,141,5.
