AdaFuse-Det: Adaptive Cross-Modal Fusion of Event Cameras for Robust Object Detection in Low-Light RGB Imagery
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Imandi, Raju B, Chethana Chakravarthi, Bharatesh Kim, Yong-Guk S, Manipriya N, Pavan Kumar B |
| author_facet | Imandi, Raju B, Chethana Chakravarthi, Bharatesh Kim, Yong-Guk S, Manipriya N, Pavan Kumar B |
| contents | Detecting objects reliably under extreme low-light conditions is an open problem in computer vision, with practical urgency in applications ranging from nighttime surveillance to search-and-rescue robotics. Conventional RGB cameras degrade sharply at low photon flux, while event cameras which record asynchronous per-pixel brightness changes at microsecond resolution and high dynamic range provide complementary structural cues that are largely illumination-invariant. We present AdaFuse-Det, a dual-stream framework that fuses CLAHE-enhanced RGB frames with voxelized event tensors through an Adaptive Cross-Modal Fusion (ACMF) module grounded in minimum-variance linear estimation theory. We formally show that the learned attention map asymptotically recovers the Gauss-Markov optimal fusion weights, and establish event conservation and temporal resolution bounds for the voxelization stage. On the LLE-VOS benchmark, AdaFuse-Det achieves a Recall of $65.54\%$, Precision of $53.85\%$, and F1-Score of $59.12\%$ under severe illumination degradation, outperforming single-modality detectors in recall by a margin that reflects the theoretically predicted illumination-adaptation behavior. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24691 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AdaFuse-Det: Adaptive Cross-Modal Fusion of Event Cameras for Robust Object Detection in Low-Light RGB Imagery Imandi, Raju B, Chethana Chakravarthi, Bharatesh Kim, Yong-Guk S, Manipriya N, Pavan Kumar B Computer Vision and Pattern Recognition Detecting objects reliably under extreme low-light conditions is an open problem in computer vision, with practical urgency in applications ranging from nighttime surveillance to search-and-rescue robotics. Conventional RGB cameras degrade sharply at low photon flux, while event cameras which record asynchronous per-pixel brightness changes at microsecond resolution and high dynamic range provide complementary structural cues that are largely illumination-invariant. We present AdaFuse-Det, a dual-stream framework that fuses CLAHE-enhanced RGB frames with voxelized event tensors through an Adaptive Cross-Modal Fusion (ACMF) module grounded in minimum-variance linear estimation theory. We formally show that the learned attention map asymptotically recovers the Gauss-Markov optimal fusion weights, and establish event conservation and temporal resolution bounds for the voxelization stage. On the LLE-VOS benchmark, AdaFuse-Det achieves a Recall of $65.54\%$, Precision of $53.85\%$, and F1-Score of $59.12\%$ under severe illumination degradation, outperforming single-modality detectors in recall by a margin that reflects the theoretically predicted illumination-adaptation behavior. |
| title | AdaFuse-Det: Adaptive Cross-Modal Fusion of Event Cameras for Robust Object Detection in Low-Light RGB Imagery |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.24691 |