摘要
针对远距离室内指示牌检测准确率较低的问题,提出一种结合超分重建的室内指示牌检测方法。分析单目相机透视成像模型发现,指示牌成像像素随拍摄距离增大而减小;通过对含有室内指示牌的图像进行超分重建,提高指示牌图像分辨率,进而提升指示牌的检测准确率。在SVHN数据集和实验室数据集下,采用SRCNN、SRResNet和SRGAN超分重建神经网络模型进行2倍和4倍超分重建的图像与未超分重建的图像进行对比,指示牌检测准确率均有明显提高。
To address the low detection accuracy of distant indoor signage,this paper proposes an indoor signage detection method with super-resolution reconstruction.Analysis of the monocular camera perspective imaging model reveals that the imaging pixels of signage decrease with increasing shooting distance.By applying super-resolution reconstruction to images containing indoor signage,the resolution of signage images is enhanced,thereby improving detection accuracy.Comparative experiments on the SVHN dataset and a laboratory dataset demonstrate that images reconstructed at 2×and 4×super-resolution using SRCNN,SRResNet,and SRGAN super-resolution reconstruction neural network model significantly outperform non-reconstructed images in signage detection accuracy.
作者
袁飞
饶娆
周光海
陈伟斌
YUAN Fei;RAO Rao;ZHOU Guanghai;CHEN Weibin(School of Automation,Guangdong Polytechnic Normal University,Guangzhou 510450,China;Guangzhou Southern Surveying and Mapping Technology Co.,Ltd.,Guangzhou 510663,China)
出处
《自动化与信息工程》
2025年第4期35-42,共8页
Automation & Information Engineering
关键词
超分重建
室内指示牌检测
相机透视成像模型
指示牌成像计算方法
目标检测算法
super-resolution reconstruction
indoor signage detection
camera perspective imaging model
signage imaging calculation method
object detection algorithms
