ADS-MIR: A Machine Perception-Oriented Visible-Infrared Sensor Fusion Framework for Intelligent Transportation Perception Under Complex Illumination Conditions

Multimodal sensor fusion in intelligent transportation systems faces severe challenges in maintaining reliable visual information acquisition under complex illumination conditions. Extreme low-light and intense glare significantly degrade visible-light sensor imaging quality, making it difficult for single-modal vision systems to maintain reliable target perception. Meanwhile, although infrared sensors provide a relatively stable saliency complement for target regions, modal discrepancies and spatial misalignment between heterogeneous visible and infrared sensors often degrade fusion performance, limiting the practical benefits of multimodal sensing for machine perception. To address these issues, this study proposes Aligned, Dual-Gated, and Saliency-Guided MIRNet (ADS-MIR), a machine perception-oriented visible-infrared sensor fusion framework that enhances the discriminability and structural representation of target regions for roadside perception sensors operating under complex conditions. Specifically, the framework employs a domain alignment layer to mitigate feature distribution discrepancies and spatial misalignment between heterogeneous sensor modalities. An illumination-guided adaptive gating mechanism dynamically modulates bimodal sensor feature contributions, while a saliency-guided frequency decoupling reinforcement strategy reinforces target-related high-frequency edge details. Experimental results on the LLVIP and M3FD datasets demonstrate that ADS-MIR improves the edge information transfer factor (QAB/F) by 49.6% to 111.6% compared with existing methods, highlighting its distinct advantage in preserving target contours and restoring edge information. Furthermore, the enhanced results provide more discriminative input features for downstream object detection, exhibiting more stable perception capabilities under complex illumination and challenging sensing scenarios.