河北水利电力学院学报2026，Vol.36Issue(3)：63-72,10.DOI:10.16046/j.cnki.issn2096-5680.2026.03.010
多层级自适应融合的建筑图像检索网络研究
Multi-level Adaptive Fusion Network for Building Image Retrieval
摘要
Abstract
A multi-level adaptive fusion network was proposed for architectural image retrieval.It ad-dressed issues such as feature extraction difficulty,loss of detailed information,and insufficient feature fu-sion.First,asymmetric convolution blocks were introduced into the ResNet50 backbone.This improved fo-cus on the target architectural areas.Then,a global information awareness module was designed.It guided the network to focus on architectural features while suppressing background noise.Next,a detail contrast enhancement mechanism was employed.Discriminative spatial features were captured from multiple per-spectives.Finally,a layer-by-layer adaptive feature fusion strategy was adopted.It refined features progres-sively,achieving effective cross-layer fusion.The proposed method achieved mAP scores of 80.84%(M)and 62.55%(H)on the ROxf dataset.It also achieved 90.04%(M)and 78.06%(H)on the RPar data-set.Robustness and accuracy in architectural image retrieval were significantly improved.关键词
建筑图像/检索网络/ResNet50/全局信息感知/特征融合/细节对比增强Key words
architectural images/image retrieval/ResNet50/global information perception/feature fusion/detail contrast enhancement分类
信息技术与安全科学引用本文复制引用
张紫琴,肖青龙,范县成,汤巍,黄洪斌..多层级自适应融合的建筑图像检索网络研究[J].河北水利电力学院学报,2026,36(3):63-72,10.基金项目
安徽省高等学校自然科学研究计划项目(2023AH052918,2024AH050637) （2023AH052918,2024AH050637）
