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Deep learning-based computer vision technology has grown stronger in recentyears, and cross-fertilization using computer vision technology has been apopular direction in recent years. The use of computer vision technology toidentify students' learning behavior in the classroom can reduce the workloadof traditional teachers in supervising students in the classroom, and ensuregreater accuracy and comprehensiveness. However, existing student learningbehavior detection systems are unable to track and detect multiple targetsprecisely, and the accuracy of learning behavior recognition is not high enoughto meet the existing needs for the accurate recognition of student behavior inthe classroom. To solve this problem, we propose a YOLOv5s network structurebased on you only look once (YOLO) algorithm to recognize and analyze students'classroom behavior in this paper. Firstly, the input images taken in the smartclassroom are pre-processed. Then, the pre-processed image is fed into thedesigned YOLOv5 networks to extract deep features through convolutional layers,and the Squeeze-and-Excitation (SE) attention detection mechanism is applied toreduce 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 ofexperiments were performed to compare with traditional learning behaviorrecognition methods to validate the effectiveness of the proposed method. Whencompared with YOLOv4, the proposed method is able to improve the mAPperformance by 11%.

Learning Behavior Recognition in Smart Classroom with Multiple Students Based on YOLOv5