光学精密工程2024，Vol.32Issue(24)：3603-3615,13.DOI:10.37188/OPE.20243224.3603
多尺度特征增强的多帧自监督单目深度估计
Multi-frame self-supervised monocular depth estimation with multi-scale feature enhancement
摘要
Abstract
The current depth estimation networks do not sufficiently extract spatial features from images in outdoor scenes,leading to issues such as object edge distortion,blurriness,and regional pseudo-shadows in the output depth maps.To address these problems,this paper proposed a multi-frame self-supervised monocular depth estimation model with multi-scale feature enhancement.Firstly,the model's encoder in-corporated an activation module based on large kernel attention to enhance its ability to extract global spa-tial features from the input image,preserving more spatial context information.Simultaneously,a structur-al enhancement module was introduced that can discriminate important features across channel dimen-sions,enhancing the network's perception of the structural characteristics of the image.Finally,the decod-er used a dynamic upsampling method instead of the traditional nearest interpolation upsampling method to restore detailed information,thereby optimizing the pseudo-shadow phenomenon in the depth map to some extent.Experimental results demonstrate that the depth estimation network proposed in this paper outper-forms current mainstream algorithms in tests on the KITTI and CityScapes outdoor datasets,particularly achieving a prediction accuracy rate of 90.3%on the KITTI dataset.Visualization results also indicate that the depth maps generated by our network model have clearer and more precise edges,effectively im-proving the prediction accuracy of the depth estimation network.关键词
单目深度估计/自监督/多帧/大核注意力/特征增强Key words
monocular depth estimation/self-supervision/multi-frame/large kernel attention/feature enhancement分类
信息技术与安全科学引用本文复制引用
寇旗旗,王伟臣,韩成功,吕晨,程德强,姬玉成..多尺度特征增强的多帧自监督单目深度估计[J].光学精密工程,2024,32(24):3603-3615,13.基金项目
国家自然科学基金项目(No.52204177) （No.52204177）
徐州市基础研究计划青年科技人才项目(No.KC23026) （No.KC23026）
