Few-Shot Object Detection Using Multimodal Sensor Systems of Unmanned Surface Vehicles
The object detection algorithm is a key component for the autonomous operation of unmanned surface vehicles (USVs). However, owing to complex marine conditions, it is difficult to obtain large-scale, fully labeled surface object datasets. Shipborne sensors are often susceptible to external interference and have unsatisfying performance, compromising the results of traditional object detection tasks. In this paper, a few-shot surface object detection method is proposed based on multimodal sensor systems for USVs. The multi-modal sensors were used for three-dimensional object detection, and the ability of USVs to detect moving objects was enhanced, realizing metric learning-based few-shot object detection for USVs. Compared with conventional methods, the proposed method enhanced the classification results of few-shot tasks. The proposed approach achieves relatively better performance in three sampled sets of well-known datasets, i.e., 2%, 10%, 5% on average precision (AP) and 28%, 24%, 24% on average orientation similarity (AOS). Therefore, this study can be potentially used for various applications where the number of labeled data is not enough to acquire a compromising result.
- Record URL:
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/14248220
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Supplemental Notes:
- © Bowei Hong et al.
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Authors:
- Hong, Bowei
- Zhou, Yuandong
- Qin, Huacheng
- Wei, Zhiqiang
- Liu, Hao
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0000-0001-5585-6576
- Yang, Yongquan
- Publication Date: 2022
Language
- English
Media Info
- Media Type: Digital/other
- Pagination: 1511
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Serial:
- Sensors
- Volume: 22
- Issue Number: 4
- Publisher: MDPI AG
- ISSN: 1424-8220
- Serial URL: http://www.mdpi.com/journal/sensors
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Algorithms; Autonomous vehicles; Data files; Multimodal transportation; Proximity detectors; Sensors
- Subject Areas: Data and Information Technology; Highways; Marine Transportation; Vehicles and Equipment;
Filing Info
- Accession Number: 01838895
- Record Type: Publication
- Files: TRIS
- Created Date: Mar 18 2022 12:17PM