Automatic Ship Detection and Tracking Considering the Uncertainty of Deep Learning-based Object Detection딥러닝 기반 객체 탐지 결과의 불확실성을 고려한 자동 선박 탐지 및 추적

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Recently, autonomous surface vehicles (ASVs) have attracted much research attention because of their potential effectiveness in carrying out various maritime missions such as surveillance and environmental monitoring. Situation awareness is a critical ability, and the camera is an essential sensor for ASVs in conducting such missions by automatically detecting and tracking objects in the surrounding environment. We detect and track the objects robustly by extracting the detection uncertainty and applying it to the tracking process. This study addresses automatic ship detection and tracking, considering the uncertainty of deep learning-based object detection using an onboard monocular camera. Gaussian YOLOv3, a deep learning-based object detection algorithm, is applied to detect a ship and estimate the uncertainty of the detection result. The detection results are sampled with uncertainty and propagated based on the camera geometry. Finally, the position of the target ship is estimated by a particle filter using the sampled detection results as measurements.
Publisher
Institute of Control, Robotics and Systems
Issue Date
2022-06
Language
Korean
Article Type
Article
Citation

Journal of Institute of Control, Robotics and Systems, v.28, no.6, pp.529 - 535

ISSN
1976-5622
DOI
10.5302/J.ICROS.2022.22.0040
URI
http://hdl.handle.net/10203/297968
Appears in Collection
ME-Journal Papers(저널논문)
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