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 performancewith somereference models in empirical experiments. Additionally,we provide two novel datasets that represent challenges in semantic segmentation for real-timeMAVtracking and infrastructure inspection tasks and verify MAVNet on these datasets. Our algorithm and datasets are made publicly available.
MAVNet: An effective semantic segmentation micro-network for MAV-based tasks / Nguyen, T.; Shivakumar, S. S.; Miller, I. D.; Keller, J.; Lee, E. S.; Zhou, A.; Ozaslan, T.; Loianno, G.; Harwood, J. H.; Wozencraft, J.; Taylor, C. J.; Kumar, V.. - In: IEEE ROBOTICS AND AUTOMATION LETTERS. - ISSN 2377-3766. - 4:4(2019), pp. 3908-3915. [10.1109/LRA.2019.2928734]
MAVNet: An effective semantic segmentation micro-network for MAV-based tasks
Loianno G.;
2019
Abstract
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 performancewith somereference models in empirical experiments. Additionally,we provide two novel datasets that represent challenges in semantic segmentation for real-timeMAVtracking and infrastructure inspection tasks and verify MAVNet on these datasets. Our algorithm and datasets are made publicly available.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
