@inproceedings{5a0c1d63d26a467c87881af975e6f2e8,
title = "Depth estimation for small obstacles based on monocular vision",
abstract = "In order to solve the problem of small obstacles avoidance in ultra-low altitude flying of unmanned aerial vehicles (UAVs), a depth estimation algorithm based on monocular visual for small tower obstacles is proposed by combining deep learning and traditional methods. With the advantage of convolutional neural networks to extract the salient features of the images, a lightweight and multi-level residual convolution neural network for obstacle segmentation is proposed. The cross-entropy loss function is optimized by adding the weight of the background and obstacles area. A method based on pairs of coplanar points is introduced to complete the depth estimation of a single frame image. Moreover, the recursive formula of sequence images depth results is derived, and a multi-frame voting optimization algorithm is proposed. Finally, the simulation results show that the proposed algorithm can effectively realize the fast segmentation and accurate depth estimation of the small tower obstacles in the sequence images.",
keywords = "Depth estimation, Monocular vision, Small obstacles, Ultra-low altitude flight",
author = "Wang Gao and Changqing Wang and Lei Li",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 3rd International Conference on Unmanned Systems, ICUS 2020 ; Conference date: 27-11-2020 Through 28-11-2020",
year = "2020",
month = nov,
day = "27",
doi = "10.1109/ICUS50048.2020.9274817",
language = "英语",
series = "Proceedings of 2020 3rd International Conference on Unmanned Systems, ICUS 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "407--411",
booktitle = "Proceedings of 2020 3rd International Conference on Unmanned Systems, ICUS 2020",
}