AI RESEARCH
DLRMamba: Distilling Low-Rank Mamba for Edge Multispectral Fusion Object Detection
arXiv CS.CV
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ArXi:2603.06920v1 Announce Type: new 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.