Categories and descriptions of student behavior.

With the continuous advancement of education informatization, classroom behavior analysis has become an important tool to improve teaching quality and student learning outcomes. However, student classroom behavior recognition methods still face challenges such as occlusion, small objects, and environmental interference, resulting in low recognition accuracy and lightweight performance. To address the above problems, this study proposes a lightweight student behavior recognition model based on Inverted Residual Mobile Block (IMRMB-Net). Specifically, this study designs a lightweight feature extraction module, IMRMB, from the images of the backbone network to be able to better capture contextual information and improve the recognition of occluded objects while saving computational resources. Using DySample, the neck network reconsiders the initial sampling position and the moving range of the offset from the point sampling perspective to accurately recognize small object behaviors in course scenes. Meanwhile, a new loss function, Focaler-ShapeIoU, is designed in this study, aiming to improve the learning ability and robustness of the model to different samples thus further solving the occlusion problem. Experiments in UK_Dataset show that IMRMB-Net has high accuracy (mAP@50 = 93.3%, mAP@50:95 = 78.7%) and lightweight performance (FPS = 60.37, Params = 7.32MB, GFLOPs = 23.8G). Meanwhile, this study verifies that IMRMB-Net can effectively solve the occlusion problem in classroom scenarios through experiments on the UK_Dataset and SCB_Dataset occlusion subsets. In addition, this study verifies the generalization ability and the ability to recognize small targets of IMRMB-Net on the VisDrone2021 dataset.随着教育信息化的持续推进，课堂行为分析已成为提升教学质量与学生学习成效的重要工具。然而，当前学生课堂行为识别方法仍面临遮挡、小目标以及环境干扰等挑战，导致识别精度与轻量化性能不足。为解决上述问题，本研究提出一种基于反向残差移动块（Inverted Residual Mobile Block）的轻量化学生行为识别模型IMRMB-Net。具体而言，本研究从骨干网络的图像特征提取环节入手，设计轻量化特征提取模块IMRMB，能够在节省计算资源的同时，更好地捕获上下文信息，提升对遮挡目标的识别能力。借助DySample，颈部网络从点采样视角重新考量初始采样位置与偏移量的移动范围，以精准识别课堂场景中的小目标行为。与此同时，本研究设计了一款新型损失函数Focaler-ShapeIoU，旨在提升模型对不同样本的学习能力与鲁棒性，进而进一步解决遮挡问题。在UK_Dataset上开展的实验结果表明，IMRMB-Net具备优异的识别精度（mAP@50 = 93.3%，mAP@50:95 = 78.7%）与轻量化性能（FPS = 60.37，参数量 = 7.32MB，GFLOPs = 23.8G）。此外，本研究通过在UK_Dataset与SCB_Dataset遮挡子集上的实验，验证了IMRMB-Net可有效解决课堂场景中的遮挡问题。除此之外，本研究还在VisDrone2021数据集上验证了IMRMB-Net的泛化能力与小目标识别能力。