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新疆大学计算机科学与技术学院
Published:2025
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[1]武爱英,伊克沙尼·帕依祖拉木,张一欣,等.公路场景下无人机遥感图像变化检测研究[J].新疆大学学报(自然科学版中英文),2025,42(04):457-468.
[1]武爱英,伊克沙尼·帕依祖拉木,张一欣,等.公路场景下无人机遥感图像变化检测研究[J].新疆大学学报(自然科学版中英文),2025,42(04):457-468. DOI: 10.13568/j.cnki.651094.651316.2024.12.20.0003.
DOI:10.13568/j.cnki.651094.651316.2024.12.20.0003.
为适应公路场景的具体需求,构建了用于公路补油变化检测的数据集ROR-CD(road oil replenishment change detection)和新的变化检测模型SIBCD(single independent branch change detection network),通过单一特征提取分支降低模型参数量.利用轻量级全局特征融合模块GFFM(global feature fusion module)融合全局信息和局部信息,提升模型面对多尺度数据的适应能力.采用基于空间和通道注意力的多尺度特征融合模块CFFM(convolutional feature fusion module)融合多时态多级特征,提升模型检测效果.在两个变化检测公开数据集LEVIR-CD和SYSU-CD以及自建公路补油变化检测数据集上进行一系列实验,验证了模型在保证计算成本的前提下,检测精度在多个数据集上均有所提高,可满足公路场景的实际需求.
To meet the specific requirements of highway scenes
a dataset named ROR-CD forroad oil replenishment change detection and a new change detection model named SIBCD are constructed. The model reduces the number of parameters through a single feature extraction branch. A lightweight GFFM is utilized to integrate global and local information
thereby enhancing the model's adaptability to multi-scale data. A multi-scale feature fusion module based on spatial and channel attention
named CFFM
is adopted to fuse multi-temporal and multi-level features
improving the model's detection performance. A series of experiments are conducted on two publicly available change detection datasets
LEVIR-CD and SYSU-CD
as well as the self-built road oil replenishment change detection dataset. The results demonstrate that the model achieves improved detection accuracy on multiple datasets while maintaining computational cost
thus meeting the practical requirements of highway scenes.
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