YOLOv5 Enhanced Learning Behavior Recognition and Analysis in Smart Classroom with Multiple Students

Zhifeng Wang, Jialong Yao, Chunyan Zeng, Wanxuan Wu, Hongmin Xu, Yang Yang
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引用次数: 8

Abstract

Deep learning-based computer vision technology has grown stronger in recent years, and cross-fertilization using computer vision technology has been a popular direction in recent years. The use of computer vision technology to identify students’ learning behavior in the classroom can reduce the workload of traditional teachers in supervising students in the classroom, and ensure greater accuracy and comprehensiveness. However, existing student learning behavior detection systems are unable to track and detect multiple targets precisely, and the accuracy of learning behavior recognition is not high enough to meet the existing needs for the accurate recognition of student behavior in the classroom. To solve this problem, we propose a YOLOv5s network structure based on you only look once (YOLO) algorithm to recognize and analyze students’ classroom behavior in this paper. Firstly, the input images taken in the smart classroom are pre-processed. Then, the pre-processed image is fed into the designed YOLOv5 networks to extract deep features through convolutional layers, and the Squeeze-and-Excitation (SE) attention detection mechanism is applied to reduce the weight of background information in the recognition process. Finally, the extracted features are classified by the Feature Pyramid Networks (FPN) and Path Aggregation Network (PAN) structures. Multiple groups of experiments were performed to compare with traditional learning behavior recognition methods to validate the effectiveness of the proposed method. When compared with YOLOv4, the proposed method is able to improve the mAP performance by 11%.
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YOLOv5增强多学生智能课堂学习行为识别与分析
基于深度学习的计算机视觉技术近年来发展壮大,利用计算机视觉技术进行交叉受精是近年来的热门方向。利用计算机视觉技术识别学生在课堂上的学习行为,可以减少传统教师在课堂上监督学生的工作量,保证更大的准确性和全面性。然而,现有的学生学习行为检测系统无法对多个目标进行精确的跟踪和检测,学习行为识别的精度也不够高,无法满足现有对课堂学生行为准确识别的需求。为了解决这一问题,本文提出了一种基于YOLO (you only look once)算法的YOLOv5s网络结构来识别和分析学生的课堂行为。首先,对智能教室中采集的输入图像进行预处理。然后,将预处理后的图像输入到设计的YOLOv5网络中,通过卷积层提取深度特征,并采用挤压激励(sse)注意检测机制降低识别过程中背景信息的权重。最后,利用特征金字塔网络(FPN)和路径聚合网络(PAN)结构对提取的特征进行分类。通过多组实验与传统的学习行为识别方法进行比较,验证了所提方法的有效性。与YOLOv4相比,该方法的mAP性能提高了11%。
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