Multi-Modal Detection and Enhancement of Teachers' and Students' Behaviors Based on YOLO Object Detection and Voiceprint Technologies

This study enhances the YOLO object detection algorithm to precisely identify students' active learning behaviors and reconstructs calculation models for teacher-student behavior ratio (Rt) and interactive behavior ratio (Ch). A multimodal approach integrating visual detection with voiceprint recognition addresses audio-video synchronization and identification challenges through mean-value algorithms and color histogram analysis. The proposed Rt-Ch teaching model distribution map and computer vision-based analytical framework enable automated identification of classroom teaching patterns, advancing traditional educational analysis toward intelligent model-algorithm systems.