A Driver Fatigue Detection Mechanism Based on a Lightweight RGB-IR Fusion Algorithm - Shao, Weixuan (Exploring Science Academic Conference Series, 2026)
To address the degradation of feature representation in a single RGB modality under low-light conditions, and the lack of texture and semantic information in a single IR modality, this paper proposes a lightweight fatigue detection method based on RGB-IR dual-modal fusion. This method realizes cross-modal information fusion at the feature layer by building a dual-stream feature extraction framework, and introduces a feature alignment mechanism to improve the semantic consistency of RGB and IR; At the same t ime, it combines lightweight network design and unidirectional feature aggregation strategy to reduce the complexity of model calculation, and improve the real-time performance of the system through asynchronous dual-stream inference mechanism. The experimental results show that the dual -flow model based on the dual lightweight strategy shows better detection robustness under extremely dark light conditions: mAP@0.5 is upgraded from 0.314 of the single - stream RGB model to 0.547, mAP@0.5:0.95 increased from 0.200 to 0.359; Compared with the dual -flow RGB IR baseline model, the number of parameters and FLOPs decreased by 37.45% and 24.57% respectively. Under the asynchronous inference mechanism, the effective frame rate of the system reaches 72.09 FPS. It can be seen that the dual lightweight strategy achieves a better balance between detection performance, model complexity and real-time performance, providing a feasible solution for in -vehicle driver fatigue detection in low-light environments.