Multimodal Object Detection Under Sparse Forest-Canopy Occlusion

• URL: http://arxiv.org/abs/2605.15326v1
• Date: Thu, 14 May 2026 18:39:51 GMT
• Title: Multimodal Object Detection Under Sparse Forest-Canopy Occlusion
• Abstract summary: This paper presents a multimodal proof-of-concept pipeline that integrates three complementary approaches.<n>A YOLOv5 detector is fine-tuned on the Teledyne FLIR thermal dataset and evaluated on thermal and fused imagery.<n>Results show that the tested terrestrial LiDAR configuration provides limited penetration for object-level detection.
• Score: 0.15469452301122175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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