期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 4, 期 4, 页码 3908-3915出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2019.2928734
关键词
Object detection; segmentation and categorization; semantic scene understanding; aerial systems: perception and autonomy; recognition; semantic segmentation
类别
资金
- Republic of Turkey Ministry of National Education
Real-time semantic image segmentation on platforms subject to size, weight, and power constraints is a key area of interest for air surveillance and inspection. In this letter, we propose MAVNet: a small, light-weight, deep neural network for real-time semantic segmentation on micro aerial vehicles (MAVs). MAVNet, inspired by ERFNet [E. Romera, J. M. lvarez, L. M. Bergasa, and R. Arroyo, ErfNet: Efficient residual factorized convnet for real-time semantic segmentation, IEEE Trans. Intell. Transp. Syst., vol. 19, no. 1, pp. 263-272, Jan. 2018.], features 400 times fewer parameters and achieves comparable performance with some reference models in empirical experiments. Additionally, we provide two novel datasets that represent challenges in semantic segmentation for real-time MAV tracking and infrastructure inspection tasks and verify MAVNet on these datasets. Our algorithm and datasets are made publicly available.
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