Self-Supervised Monocular Depth Estimation for Traffic Scenes Based on Dual Attention Mechanism and Adaptive Cost Volume
Aiming at the problems of self-supervised monocular depth estimation in current traffic scenarios,such as weak feature expression ability,fuzzy local details of depth map and low accuracy of depth estimation,a self-supervised monocular depth estimation method based on dual attention mechanism and adaptive cost volume is proposed.Firstly,a du-al attention mechanism combining channel attention and spatial attention is used to adaptively weight the extracted scene features to enhance the feature expression ability of the feature extraction network.Secondly,according to the adaptively constructed cost volume of extracting global features,the network is guided to learn fine depth features,which improves the learning ability of the network model for the local details of the depth map and solves the problem of low accuracy of exist-ing depth estimation methods.Experimental results on public datasets KITTI and Cityscapes show that the proposed meth-od is superior to the current mainstream methods.
国家科技期刊平台
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