Open AccessA Robust Monocular Depth Estimation Framework Based on Light-Weight ERF-Pspnet for Day-Night Driving Scenes
Author(s) -
Keyang Zhou,
Kaiwei Wang,
Kailun Yang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1518/1/012051
Subject(s) - monocular , artificial intelligence , computer science , task (project management) , convolutional neural network , computer vision , depth map , depth perception , frame rate , estimation , matching (statistics) , monocular vision , deep learning , frame (networking) , image (mathematics) , perception , mathematics , engineering , statistics , telecommunications , systems engineering , neuroscience , biology
With the development of deep learning, various fields of computer vision have made huge progress. Among them, depth estimation is an important part of scene perception, therefore receives much interest and is widely used in daily life with the assistance of GPUs. Besides, the ways to obtain depth maps have also been improved, from using multiple images to a single image to obtain depth, which is called monocular depth estimation task. In this paper, we design a convolutional neural network called ERF-PSPNet to perform the task. We prove that by using unsupervised training, monocular depth estimation’s result learned from large-scale dataset is close to the result of stereo matching. We also show that the monocular depth estimation model proposed in this paper can achieve a satisfying precision while maintaining a certain real-time frame rate for day-night driving scenes, which confirms the practical applicability of our design and result.