Student Classroom Behavior Recognition Based on Improved YOLOv8s

TL;DR

This paper introduces an improved YOLOv8s-based model for recognizing student classroom behavior, addressing challenges like dense targets, occlusions, and class imbalance in complex scenes.

Contribution

The paper proposes novel modules and loss functions to enhance detection accuracy and minority class recognition in classroom behavior analysis.

Findings

Achieved 1.8% higher mAP50 over baseline

Achieved 2.1% higher mAP50-95 over baseline

Outperformed several mainstream detection methods

Abstract

In classroom teaching, student behavior can reflect their learning state and classroom participation, which is of great significance for teaching quality analysis. To address the problems of dense student targets, numerous small objects, frequent occlusions, and imbalanced class distribution in real classroom scenes, this paper proposes an improved student classroom behavior recognition model named ALC-YOLOv8s based on YOLOv8s. The model introduces SPPF-LSKA to enhance contextual feature extraction, employs CFC-CRB and SFC-G2 to optimize multi-scale feature fusion, and incorporates ATFLoss to improve the learning ability for minority classes and hard samples. Experimental results show that compared with the baseline model, the improved model achieves increases of 1.8% in mAP50 and 2.1% in mAP50-95. Compared with several mainstream detection methods, the proposed model can well meet the requirements of automatic student behavior recognition in complex classroom scenarios.