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方向感知增强的轻量级自监督单目深度估计方法

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为解决现有单目深度估计网络复杂度高、在弱纹理区域精度低等问题,该文提出一种基于方向感知增强的轻量级自监督单目深度估计方法(DAEN).首先,引入迭代扩展卷积模块(IDC)作为编码器的主体,提取远距离像素的相关性;其次,设计方向感知增强模块(DAE)增强垂直方向的特征提取,为深度估计模型提供更多的深度线索;此外,通过聚合视差图特征改善解码器上采样过程中的细节丢失问题;最后,采用特征注意力模块(FAM)连接编解码器,有效利用全局上下文信息解决弱纹理区域的不适应问题.在KITTI数据集上的实验结果表明,该文模型参数量仅2.9M,取得δ指标89.2%的先进性能.在Make3D数据集上验证DAEN的泛化性,结果表明,该文模型各项指标均优于目前主流的方法,在弱纹理区域具有更好的深度预测性能.
Lightweight Self-supervised Monocular Depth Estimation Method with Enhanced Direction-aware
To address challenges such as high complexity in monocular depth estimation networks and low accuracy in regions with weak textures,a Direction-Aware Enhancement-based lightweight self-supervised monocular depth estimation Network(DAEN)is proposed in this paper.Firstly,the Iterative Dilated Convolution module(IDC)is introduced as the core of the encoder to extract correlations among distant pixels.Secondly,the Directional Awareness Enhancement module(DAE)is designed to enhance feature extraction in the vertical direction,providing the depth estimation model with additional depth cues.Furthermore,the problem of detail loss during the decoder upsampling process is addressed through the aggregation of disparity map features.Lastly,the Feature Attention Module(FAM)is employed to connect the encoder and decoder,effectively leveraging global contextual information to resolve adaptability issues in regions with weak textures.Experimental results on the KITTI dataset demonstrate that the proposed method has a model parameter count of only 2.9M,achieving an advanced performance with 6 metric of 89.2%.The generalization of DAEN is validated on the Make3D datasets,with results indicating that the proposed method outperforms current state-of-the-art methods across various metrics,particularly exhibiting superior depth prediction performance in regions with weak textures.

Image processingDepth estimationSelf-supervised learningDirection-aware

程德强、徐帅、吕晨、韩成功、江鹤、寇旗旗

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中国矿业大学信息与控制工程学院 徐州 221116

中国矿业大学计算机科学与技术学院 徐州 221116

图像处理 深度估计 自监督学习 方向感知

2024

电子与信息学报
中国科学院电子学研究所 国家自然科学基金委员会信息科学部

电子与信息学报

CSTPCD北大核心
影响因子:1.302
ISSN:1009-5896
年,卷(期):2024.46(9)