Multimodal Object Detection Under Sparse Forest-Canopy Occlusion
Reliable detection of humans beneath forest canopy remains difficult due to occlusion. This paper presents a multimodal pipeline integrating LiDAR evaluation, visible-thermal fusion, and synthetic-aperture imaging via AOS. A YOLOv5 detector fine-tuned on FLIR dataset achieves mAP ~0.83, establishing a baseline for UAV-based search-and-rescue and surveillance in forested environments.
[2605.15326] Multimodal Object Detection Under Sparse Forest-Canopy Occlusion
[Submitted on 14 May 2026]
Title:Multimodal Object Detection Under Sparse Forest-Canopy Occlusion
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Abstract:Reliable detection of humans beneath forest canopy remains a difficult remote-sensing challenge due to sparse, structured, and viewpoint-dependent occlusion. This paper presents a multimodal proof-of-concept pipeline that integrates three complementary approaches: (i) experimental evaluation of LiDAR returns through vegetation to assess the feasibility of active sensing, (ii) visible--thermal image fusion using a multi-scale transform and sparse-representation framework to enhance human saliency, and (iii) synthetic-aperture image formation via Airborne Optical Sectioning (AOS) to suppress canopy clutter. A YOLOv5 detector is fine-tuned on the Teledyne FLIR thermal dataset and evaluated on thermal and fused imagery. Results show that the tested terrestrial LiDAR configuration provides limited penetration for object-level detection, while visible--thermal fusion improves target visibility in low-contrast scenes and AOS enhances ground-plane detection in synthetic forest imagery. The fine-tuned YOLOv5 achieves a mean average precision of $\sim$0.83 on the top three FLIR classes. These findings establish an initial baseline for UAV-deployable search-and-rescue and surveillance systems operating in forested environments, and motivate future work on dedicated forest datasets and real-time multimodal integration.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.15326 [cs.CV]
(or arXiv:2605.15326v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.15326
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mangal Kothari [view email] [v1] Thu, 14 May 2026 18:39:51 UTC (7,830 KB)
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