MAVNet: An effective semantic segmentation micro-network for MAV-based tasks

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Nguyen T., Shivakumar S. S. , Miller I. D. , Keller J., Lee E. S. , Zhou A., ...More

IEEE Robotics and Automation Letters, vol.4, no.4, pp.3908-3915, 2019 (Peer-Reviewed Journal) identifier

  • Publication Type: Article / Article
  • Volume: 4 Issue: 4
  • Publication Date: 2019
  • Doi Number: 10.1109/lra.2019.2928734
  • Journal Name: IEEE Robotics and Automation Letters
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.3908-3915


© 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.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.