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Image-based anomaly detection using β -Variational Autoencoder for surface vehicle collision avoidance

Ahlqvist, Johan and Skoog, André (2020)
Department of Automatic Control
Abstract
Unmanned vehicles need robust systems to ensure the safety of the vehicle and its environment. Being able to find and avoid perilous situations is paramount to such a system. In this paper we suggest an unsupervised image-based anomaly detection algorithm using a variational autoencoder and a superpixel segmentation algorithm, which is adapted to the maritime obstacle detection task. The algorithm locates potentially hazardous objects and calculates the distance to them by measuring the images’ reconstruction error over segmented regions. The algorithm’s results on the public MODD2 dataset shows that it has difficulties finding small objects and that it cannot compete with the current state-of-the-art supervised segmentation algorithms on... (More)
Unmanned vehicles need robust systems to ensure the safety of the vehicle and its environment. Being able to find and avoid perilous situations is paramount to such a system. In this paper we suggest an unsupervised image-based anomaly detection algorithm using a variational autoencoder and a superpixel segmentation algorithm, which is adapted to the maritime obstacle detection task. The algorithm locates potentially hazardous objects and calculates the distance to them by measuring the images’ reconstruction error over segmented regions. The algorithm’s results on the public MODD2 dataset shows that it has difficulties finding small objects and that it cannot compete with the current state-of-the-art supervised segmentation algorithms on the same dataset, with an F1 score of 26.6% compared to 82.7%. Although further research and optimization is required to utilize the algorithm in a production level product, the results indicate that the algorithm is worth investigating further
as it is able to detect many of the objects in our testing videos and due to it having applications in several areas. (Less)
Please use this url to cite or link to this publication:
author
Ahlqvist, Johan and Skoog, André
supervisor
organization
year
type
H3 - Professional qualifications (4 Years - )
subject
report number
TFRT-6121
other publication id
0280-5316
language
English
id
9033151
date added to LUP
2020-12-23 11:18:06
date last changed
2020-12-23 11:18:06
@misc{9033151,
  abstract     = {{Unmanned vehicles need robust systems to ensure the safety of the vehicle and its environment. Being able to find and avoid perilous situations is paramount to such a system. In this paper we suggest an unsupervised image-based anomaly detection algorithm using a variational autoencoder and a superpixel segmentation algorithm, which is adapted to the maritime obstacle detection task. The algorithm locates potentially hazardous objects and calculates the distance to them by measuring the images’ reconstruction error over segmented regions. The algorithm’s results on the public MODD2 dataset shows that it has difficulties finding small objects and that it cannot compete with the current state-of-the-art supervised segmentation algorithms on the same dataset, with an F1 score of 26.6% compared to 82.7%. Although further research and optimization is required to utilize the algorithm in a production level product, the results indicate that the algorithm is worth investigating further
as it is able to detect many of the objects in our testing videos and due to it having applications in several areas.}},
  author       = {{Ahlqvist, Johan and Skoog, André}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Image-based anomaly detection using β -Variational Autoencoder for surface vehicle collision avoidance}},
  year         = {{2020}},
}