MGS-DETR：一种用于联合检测驾驶员疲劳和分心的多粒度空间表示学习框架

Driver fatigue and distracted driving are major causes of traffic accidents, yet existing vision-based detectors often suffer from imbalanced feature modeling, insufficient cross-layer fusion, and loss of fine-grained spatial details under complex in-vehicle conditions. To address these issues, we propose MGS-DETR, a multi-granularity spatial representation learning framework built upon RT-DETR for joint driver fatigue and distraction detection. In the encoding stage, we develop a Multi-scale Spatial Awareness Encoder (MSAE) to balance local fine-grained fatigue cues and global distracted-acti