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Multimodal vehicle detection: fusing 3D-LIDAR and color camera data
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“More recently, only a few multimodal-based multi-stage detection frameworks [191–193] have been developed and have achieved outstanding detection performance on benchmark datasets.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [192]
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Metadata
- Authors
- Asvadi, A., Garrote, L., Premebida, C., Peixoto, P., & J. Nunes, U.
- Affiliations
- University of Coimbra (PT)
- Corresponding author
- Asvadi, Alireza
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 115
- Pages
- 20-29
- ISSN
- 0167-8655 (ISSN-L)
- OpenAlex
- W2760327656
- Cited by
- 225
- References
- 52
- Citations by year
- 2026: 12 · 2025: 14 · 2024: 28 · 2023: 20 · 2022: 44 · 2021: 43 · 2020: 34 · 2019: 22 · 2018: 7
- FWCI
- 11.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pattern Recognition Letters (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- vehicle detection, multimodal fusion, 3D LiDAR, depth images, reflectance map, RGB camera, decision fusion, autonomous vehicles, sensor fusion, bounding box detection, KITTI dataset
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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