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Mamba-based fusion model enhances infrared vehicle detection

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new Mamba-based model for fusing long-wave infrared polarimetric remote sensing images, aiming to improve vehicle detection. This model effectively integrates infrared intensity and polarization information, addressing limitations of existing methods that can introduce noise or fail to capture complementary data. The approach utilizes bidirectional state space scanning and incorporates various loss functions to suppress noise while preserving crucial target features. A new dataset, AIR-LWIR-Pol, was created for evaluation, demonstrating that the proposed fusion method enhances performance on metrics like entropy and structural similarity, leading to improved vehicle detection accuracy with the YOLOv11 model. AI

IMPACT Introduces a novel fusion technique for remote sensing imagery, potentially improving autonomous systems and surveillance capabilities.

RANK_REASON The cluster contains an academic paper detailing a new model and dataset for a specific computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Mamba-based fusion model enhances infrared vehicle detection

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The cluster contains an academic paper detailing a new model and dataset for a specific computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Wenjuan Zhang, Xiangrui Peng, Zhen Li, Caimeng Yi, Xuesong Ma, Jianwei Wang, Jie Pan ·

    Long-wave Infrared Polarimetric Remote Sensing Image Fusion for Vehicle Detection

    arXiv:2610.27018v1 Announce Type: new Abstract: Blurred vehicle contours and low contrast limit detection performance in infrared remote sensing imagery, while existing fusion methods may introduce polarization noise and fail to integrate complementary information effectively. To…

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