🤖 AI Summary
This work proposes ALC-YOLOv8s to address challenges in real-world classroom settings, including dense student targets, numerous small objects, frequent occlusions, and imbalanced behavior categories. Building upon YOLOv8s, the model integrates an SPPF-LSKA module to enhance contextual awareness, introduces CFC-CRB and SFC-G2 structures to refine multi-scale feature fusion, and employs ATFLoss to improve learning on minority classes and hard samples. Experimental results demonstrate that the proposed method outperforms the baseline by 1.8% in mAP50 and 2.1% in mAP50–95, surpassing several state-of-the-art detectors and effectively meeting the demand for high-accuracy behavior recognition in complex classroom environments.
📝 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.