MAVNet: An Effective Semantic Segmentation Micro-Network for MAV-Based Tasks

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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 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.
Funding Information
  • MAST Collaborative Technology Alliance - Contract (W911NF-08-2-0004)
  • ARL (W911NF-08-2-0004)
  • ONR (N00014-07-1-0829, N00014-14-1-0510)
  • ARO (W911NF-13-1-0350)
  • NSF (IIS-1426840, IIS-1138847)
  • DARPA (HR001151626, HR0011516850)
  • Semiconductor Research Corporation

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