Student classroom behavior recognition based on bidirectional feature fusion

Yuang Duan; Shengnan Zhang

doi:10.1117/12.3031292

19 July 2024 Student classroom behavior recognition based on bidirectional feature fusion

Yuang Duan, Shengnan Zhang

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131810J (2024) https://doi.org/10.1117/12.3031292
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

In recent years, deep learning has shown remarkable success in the field of object detection, with the YOLO series models known for their speed and effective recognition. A classroom behavior recognition model based on bidirectional feature fusion is proposed, enhancing the YOLOv5 model by incorporating bidirectional feature fusion, attention mechanisms, and modifying the loss function. Experimental results indicate that the improved model achieves a maximum average recognition accuracy of 97%, demonstrating a notable increase of 4.8% compared to the original model, highlighting a significant improvement in accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuang Duan and Shengnan Zhang "Student classroom behavior recognition based on bidirectional feature fusion", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131810J (19 July 2024); https://doi.org/10.1117/12.3031292

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KEYWORDS

Feature fusion

Object detection

Feature extraction

Data processing

Detection and tracking algorithms

Performance modeling

Education and training

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