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; Joseph H. Harwood; Jennifer Wozencraft; Camillo J. Taylor; Vijay Kumar

doi:10.1109/LRA.2019.2928734

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, Joseph H. Harwood, Jennifer Wozencraft, Camillo J. Taylor, Vijay Kumar

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Article number	8764006
Pages (from-to)	3908-3915
Number of pages	8
Journal	IEEE Robotics and Automation Letters
Volume	4
Issue number	4
DOIs	https://doi.org/10.1109/LRA.2019.2928734
State	Published - Oct 2019

Keywords

Aerial systems: perception and autonomy
Object detection
Recognition
Segmentation and categorization
Semantic scene understanding
Semantic segmentation

ASJC Scopus subject areas

Control and Systems Engineering
Biomedical Engineering
Human-Computer Interaction
Mechanical Engineering
Computer Vision and Pattern Recognition
Computer Science Applications
Control and Optimization
Artificial Intelligence

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10.1109/LRA.2019.2928734

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title = "MAVNet: An effective semantic segmentation micro-network for MAV-based tasks",

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.",

keywords = "Aerial systems: perception and autonomy, Object detection, Recognition, Segmentation and categorization, Semantic scene understanding, Semantic segmentation",

author = "Ty Nguyen and Shivakumar, {Shreyas S.} and Miller, {Ian D.} and James Keller and Lee, {Elijah S.} and Alex Zhou and Tolga {\"O}zaslan and Giuseppe Loianno and Harwood, {Joseph H.} and Jennifer Wozencraft and Taylor, {Camillo J.} and Vijay Kumar",

year = "2019",

month = oct,

doi = "10.1109/LRA.2019.2928734",

language = "English (US)",

volume = "4",

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T2 - An effective semantic segmentation micro-network for MAV-based tasks

AU - Nguyen, Ty

AU - Shivakumar, Shreyas S.

AU - Miller, Ian D.

AU - Keller, James

AU - Lee, Elijah S.

AU - Zhou, Alex

AU - Özaslan, Tolga

AU - Loianno, Giuseppe

AU - Harwood, Joseph H.

AU - Wozencraft, Jennifer

AU - Taylor, Camillo J.

AU - Kumar, Vijay

PY - 2019/10

Y1 - 2019/10

N2 - 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.

AB - 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.

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KW - Object detection

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KW - Segmentation and categorization

KW - Semantic scene understanding

KW - Semantic segmentation

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

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Keywords

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