Abstract
Depth estimation from monocular image plays an essential role in artificial intelligence, which is one of the important ways for sensing the operating environment in automatic-driving system or advanced driving assistant system. The most recent approaches have gained significant improvement for depth prediction based on convolutional neural networks (CNNs). In this paper, a novel framework of CNNs is proposed for monocular depth estimation based on deep ordinal regression network (DORN) and a U-net structure. The new model is trained, verified in process and tested on 5000 images from a simulation experiment platform provide by “Grand Theft Auto”. To eliminate or at least largely reduce the impact from ground truth with no depth values, three different training strategies were employed for network optimization. We developed an effective weighted training strategy for depth prediction to improve the estimation accuracy. The comparison of evaluations over our results and DORN demonstrated the effectiveness of our method. The results showed that the proposed method achieved state-of-the-art performances.
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Acknowledgements
this research is supported by Natural Science Foundation of Beijing “Research on the Planning Decision Making Supporting Approaches of Healthy City Planning of Beijing Based on the Analysis of Social Sensing Data” (No. 8182027), and open fund of Institute for China Sustainable Urbanization, Tsinghua University:“Pre-study on new urban development strategy integrating multi-source big data” (TUCSU-K-17026-01). We are also grateful for the computational resources provided by GTA.
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Kang, J. et al. (2020). An Improved Convolutional Neural Network for Monocular Depth Estimation. In: Wang, W., Baumann, M., Jiang, X. (eds) Green, Smart and Connected Transportation Systems. Lecture Notes in Electrical Engineering, vol 617. Springer, Singapore. https://doi.org/10.1007/978-981-15-0644-4_94
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DOI: https://doi.org/10.1007/978-981-15-0644-4_94
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