MobileMamba-DETR: Efficient Dual-Modal Vehicle Detection for Autonomous Driving via Multi-Scale Selective State Space Fusion

Robust autonomous-driving detection requires using RGB texture and infrared thermal cues without sacrificing real-time inference. Existing RGB-IR detectors often rely on static feature concatenation or quadratic attention, which makes them sensitive to modality imbalance, small spatial offsets, and deployment cost. We propose MobileMamba-DETR, a lightweight DETR-style detector that treats dual-modal fusion as a selective state-space process. Its principal design is an SS2D-based cross-modal interaction module that uses normalized RGB-IR contrast as a guide, while a MobileMamba backbone, spectral–spatial encoder, and dynamic convolutional decoder provide efficient multi-scale representation and query localization. On M3FD and FLIR-ADAS, MobileMamba-DETR achieves mAP${}_{50}$ of 83.6% and 78.3%, respectively, with 38.7M parameters and 42 FPS inference at $640\times 640$ on an RTX 3090. The results, ablations, and seed-based validation show that selective state-space fusion improves accuracy while retaining real-time throughput.