计算机工程与应用2025，Vol.61Issue(11)：132-143,12.DOI:10.3778/j.issn.1002-8331.2409-0183
基于在线增强和跨尺度特征重建的雾天目标检测
Target Detection in Foggy Conditions Based on Online Enhancement and Cross-Scale Feature Reconstruction
摘要
Abstract
A foggy target detection method EC-RTDETR is proposed to overcome the problem that the overall contrast of foggy images is low and the images are seriously interfered by noise,which leads to the poor detection effect of target detection algorithm.Firstly,an online defogging network DDNet is designed to remove the noise interference while enhancing the texture information of the image by using jump connections and contour feature computation.Secondly,light-weight FasterNet is introduced as a feature extraction network to fully extract the target spatial features and improve the computational efficiency of the model.Then,efficient additive attention is used instead of the AIFI of the original network to achieve richer shallow and deep feature interaction.Finally,a context-guided cross-scale feature reconstruction fusion module is proposed to enable a multi-scale network design to extract contextual information,and the extracted fea-tures are fused and reconstructed to highlight the foggy target features and improve the detection effect of the algorithm in foggy scenes.Experiments on RTTS dataset show that compared with the benchmark RTDETR,mAP is improved by 2.35 percentage points,mmAP is improved by 3.66 percentage points,Recall is improved by 4.71 percentage points,and the amount of parameters is reduced by 9.82×106,which proves that EC-RTDETR effectively improves the performance of target detection in foggy sky scenes while realizing light-weighting.关键词
图像去雾/目标检测/RTDETR/跨尺度特征重建/轻量化网络Key words
image defogging/object detection/RTDETR/cross-scale feature reconstruction/light-weighting network分类
计算机与自动化引用本文复制引用
朱开源,吴佰靖,高德勇,刘媛,王付祥..基于在线增强和跨尺度特征重建的雾天目标检测[J].计算机工程与应用,2025,61(11):132-143,12.基金项目
国家自然科学基金(62067006,62367005) （62067006,62367005）
甘肃省知识产权计划项目(21ZSCQ013) （21ZSCQ013）
甘肃省高校科研创新平台重大培育项目(2024CXPT-17). （2024CXPT-17）
