Exploring RGB+Depth Fusion for Real-Time Object Detection
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In this paper, we investigate whether fusing depth information on top of normal RGB data for camera-based object detection can help to increase the performance of current state-of-the-art single-shot detection networks. Indeed, depth sensing is easily acquired using depth cameras such as a Kinect or stereo setups. We investigate the optimal manner to perform this sensor fusion with a special focus on lightweight single-pass convolutional neural network (CNN) architectures, enabling real-time processing on limited hardware. For this, we implement a network architecture allowing us to parameterize at which network layer both information sources are fused together. We performed exhaustive experiments to determine the optimal fusion point in the network, from which we can conclude that fusing towards the mid to late layers provides the best results. Our best fusion models significantly outperform the baseline RGB network in both accuracy and localization of the detections.
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Experimental results show that using depth information, optical flow information, and LiDAR point clouds in addition to conventional RGB data can improve the performance of one-stage based detection systems.
“According to experimental results, it has been shown that the use of depth information [183, 184], optical flow information [185], and LiDAR point clouds [186] in addition to conventional RGB data can improve the performance of one-stage based detection systems.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [183]
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- Authors
- Ophoff, T., Van Beeck, K., & Goedemé, T.
- Affiliations
- KU Leuven (BE)
- EAVISE, KU Leuven, 2860 Sint-Katelijne-Waver, Belgium
- Corresponding author
- Ophoff, Tanguy
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-02-19
- Volume
- 19
- Issue
- 4
- Pages
- 866
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6412390
- OpenAlex
- W2916052001
- Cited by
- 77
- References
- 35
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 8 · 2023: 10 · 2022: 16 · 2021: 17 · 2020: 8 · 2019: 2
- FWCI
- 4.80
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Lirias (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Agentschap Innoveren en Ondernemen
- Fonds Wetenschappelijk Onderzoek (S003817N)
- KU Leuven
- Topics
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- RGB-D fusion, sensor fusion, object detection, single-shot detection, depth camera, lightweight convolutional neural network, real-time detection
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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