Zhang, X.; Nie, J.; Wei, S.; Zhu, G.; Dai, W.; Yang, C. A Study of Classroom Behavior Recognition Incorporating Super-Resolution and Target Detection. Sensors2024, 24, 5640.
Zhang, X.; Nie, J.; Wei, S.; Zhu, G.; Dai, W.; Yang, C. A Study of Classroom Behavior Recognition Incorporating Super-Resolution and Target Detection. Sensors 2024, 24, 5640.
Zhang, X.; Nie, J.; Wei, S.; Zhu, G.; Dai, W.; Yang, C. A Study of Classroom Behavior Recognition Incorporating Super-Resolution and Target Detection. Sensors2024, 24, 5640.
Zhang, X.; Nie, J.; Wei, S.; Zhu, G.; Dai, W.; Yang, C. A Study of Classroom Behavior Recognition Incorporating Super-Resolution and Target Detection. Sensors 2024, 24, 5640.
Abstract
With the development of educational technology, machine learning and deep learning provide technical support for traditional classroom observation assessment. However, in real classroom scenarios, the technique faces challenges such as lack of clarity of raw images, complexity of datasets, multi-target detection errors and complexity of character interactions.Based on the above problems, a student classroom behavior recognition network incorporating super-resolution and target detection is proposed. To cope with the problem of unclear original images in the classroom scenario, SRGAN (Super Resolution Generative Adversarial Network for Images) is used to improve the image resolution and thus the recognition accuracy.To address the dataset complexity and multi-targeting problems, feature extraction is optimized and multi-scale feature recognition is enhanced by introducing AKConv and LASK attention mechanisms into the Backbone module of YOLOv8s algorithm. To improve the character interaction complexity problem, the CBAM attention mechanism is integrated to enhance the recognition of important feature channels and spatial regions. Experiments show that it can detect six behaviors of students raising their hands, reading, writing, playing cell phones, looking down, and lying on the table in high-definition images. And the accuracy and robustness of this network are verified.Compared with small target detection algorithms such as Faster R-CNN, YOLOv5, and YOLOv8s, this network has better detection performance and can efficiently deal with the behavior recognition of multiple students.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
