Abstract: To address the challenges of large-scale variations in student behaviors, dense target distributions, and insufficient recognition accuracy for back-row students in classroom scenarios, this paper proposes an improved student behavior recognition algorithm, termed MSD-YOLO, based on the YOLO11n baseline model. First, a Multi-Scale Behavior Perception module is introduced into the backbone to enhance the network’s ability to perceive behavior features at different scales, thereby alleviating the scale inconsistency between front-row and back-row students during the feature extraction stage. Second, a Semantic–Spatial Deep Fusion module is designed in the neck to strengthen the interaction between high-level semantic information and low-level spatial details, improving the discriminative representation of features in dense classroom scenes. Finally, a Dual-Scale Context Aggregation module is embedded before each detection head. By integrating global contextual information with a feature re-calibration mechanism, the proposed module further enhances the network’s ability to distinguish small-scale student behaviors, thereby improving the recognition accuracy of back-row students during the detection stage. Experimental results demonstrate that, compared with the YOLO11n baseline, MSD-YOLO achieves improvements of 3.2% and 3.7% in mAP@0.5 and mAP@0.5:0.95, respectively, on the self-constructed dataset. On the public STBD-08 dataset, the corresponding improvements reach 2.4% and 2.6%. Moreover, with only a slight increase in computational cost and model parameters, the proposed method maintains favorable real-time performance, validating its effectiveness and practical value for classroom student behavior recognition tasks

摘要： 针对课堂场景中学生行为尺度差异较大、分布密集以及后排学生行为识别精度不足等问题，本文在YOLO11n基线模型的基础上提出了一种改进的课堂学生行为识别算法：MSD-YOLO。首先，在Backbone部分引入多尺度行为感知模块，增强网络对不同尺度行为特征的感知能力，在特征提取阶段缓解前后排学生尺度不一致带来的影响。其次，在Neck部分设计语义-空间深度融合模块，从而加强高层语义信息与低层空间细节之间的交互，提升密集场景下特征表达的判别性。最后，在Head部分的每个检测头前引入双尺度上下文聚合模块，通过整合全局上下文信息与特征重标定机制，进一步增强网络对目标较小学生的行为区分能力，从而在检测阶段提升网络对后排学生行为的识别精准度。实验结果表明，与YOLO11n 基线模型相比，MSD-YOLO在自建数据集上的mAP@0.5和mAP@0.5:0.95分别提升了3.2%和3.7%；在公开数据集STBD-08上相较于基线模型，mAP@0.5和mAP@0.5:0.95分别提升2.4%和2.6%。同时，在引入较少计算量和参数的前提下，算法仍保持良好的实时性能，验证了改进算法在课堂学生行为识别任务中的有效性与实用价值。