SiT-YOLOv9: An Efficient Algorithm for Learning Behavior Detection in the Home Environment

Abstract

In the context of home-based learning, accurate identification of learning behaviors is essential for enhancing post-classroom learning efficiency. However, due to background interference and computational constraints in the TinyML terminal within home environments, CNN-based algorithms are susceptible to reduced performance and accuracy, leading to an increased false positive rate. To address this challenge, we propose a lightweight detection model called SiT-YOLOv9, which integrates MODNet, image enhancement, and other modules into the YOLOv9 model while also implementing moderate network pruning to effectively mitigate issues related to image noise and training sample computational power. Evaluation of the SiTBehaviors video dataset demonstrates that the SiT YOLOv9 model achieves outstanding performance with a recognition accuracy of 0.948 (mAP50) at a high processing speed of 90.9 frames per second. When compared with original models such as YOLOv8, YOLOv10, and RT-DERT, our proposed model exhibits superior recognition accuracy of 0.948 mAP, and a processing speed of 0.2 ms.