DLRMamba: Distilling Low-Rank Mamba for Edge Multispectral Fusion Object Detection

• URL: http://arxiv.org/abs/2603.06920v1
• Date: Fri, 06 Mar 2026 22:43:57 GMT
• Title: DLRMamba: Distilling Low-Rank Mamba for Edge Multispectral Fusion Object Detection
• Abstract summary: Multispectral fusion object detection is a critical task for edge-based maritime surveillance and remote sensing.<n>Current State Space Models (SSMs) like Mamba suffer from significant parameter redundancy in their standard 2D Selective Scan (SS2D) blocks.<n>We propose the Low-Rank Two-Dimensional Selective Structured State Space Model (Low-Rank SS2D), which reformulates state transitions via matrix factorization to exploit intrinsic feature sparsity.
• Score: 7.883488411699879
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multispectral fusion object detection is a critical task for edge-based maritime surveillance and remote sensing, demanding both high inference efficiency and robust feature representation for high-resolution inputs. However, current State Space Models (SSMs) like Mamba suffer from significant parameter redundancy in their standard 2D Selective Scan (SS2D) blocks, which hinders deployment on resource-constrained hardware and leads to the loss of fine-grained structural information during conventional compression. To address these challenges, we propose the Low-Rank Two-Dimensional Selective Structured State Space Model (Low-Rank SS2D), which reformulates state transitions via matrix factorization to exploit intrinsic feature sparsity. Furthermore, we introduce a Structure-Aware Distillation strategy that aligns the internal latent state dynamics of the student with a full-rank teacher model to compensate for potential representation degradation. This approach substantially reduces computational complexity and memory footprint while preserving the high-fidelity spatial modeling required for object recognition. Extensive experiments on five benchmark datasets and real-world edge platforms, such as Raspberry Pi 5, demonstrate that our method achieves a superior efficiency-accuracy trade-off, significantly outperforming existing lightweight architectures in practical deployment scenarios.

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