Multimodal Object Detection in Remote Sensing
Not on the map. Why?
Abstract (arXiv)
Object detection in remote sensing is a crucial computer vision task that has seen significant advancements with deep learning techniques. However, most existing works in this area focus on the use of generic object detection and do not leverage the potential of multimodal data fusion. In this paper, we present a comparison of methods for multimodal object detection in remote sensing, survey available multimodal datasets suitable for evaluation, and discuss future directions.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 18 references that are in the map. First posted on arXiv 13 Jul 2023.
Builds on
8 of 18 references.
References · 8
- 2019Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmarkcited by 340
- 2021LLVIP: A Visible-infrared Paired Dataset for Low-light Visioncited by 231
- 2021Object Detection in Aerial Images: A Large-Scale Benchmark and Challengescited by 204
- 2020Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blockscited by 85
- 2022Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Reviewcited by 73
- 2021SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Watercited by 61
- 2022SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagerycited by 47
- 2021Cross-Modality Attentive Feature Fusion for Object Detection in Multispectral Remote Sensing Imagerycited by 45
Built on by
Citing papers per year: 2025: 1.
1 paper.