MAVNet: An Effective Semantic Segmentation Micro-Network for MAV-Based Tasks
Ty Nguyen, Shreyas S. Shivakumar, Ian D. Miller, James Keller, Elijah S. Lee, Alex Zhou, Tolga Özaslan, Giuseppe Loianno
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,” <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Trans. Intell. Transp. Syst.</i> , 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.
BibTeX
@inproceedings{ral2019_mavnetaneffectiv,
title = {MAVNet: An Effective Semantic Segmentation Micro-Network for MAV-Based Tasks},
author = {Ty Nguyen and Shreyas S. Shivakumar and Ian D. Miller and James Keller and Elijah S. Lee and Alex Zhou and Tolga Özaslan and Giuseppe Loianno and Joseph H. Harwood and Jennifer M. Wozencraft and Camillo J. Taylor and Vijay Kumar},
booktitle = {RA-L 2019},
year = {2019}
}