关键词: 课堂行为检测, 高精度, YOLO11, 多维度特征流动网络（MFFN）, 特征增强聚合模块（FEAM）

Abstract: Aiming at the problem that student targets in classroom scenes are small, densely distributed, and easily obscured, resulting in low detection accuracy and poor recognition effect, an improved classroom behavior detection algorithm based on YOLO11 (you only look once version 11), named MFD-YOLO, is proposed. Through a series of innovative designs, this algorithm significantly improves the accuracy of classroom behavior detection and recognition effects. Firstly, a multi-dimensional feature flow network (MFFN) is designed to enhance the feature representation of small targets by combining the dimension-aware selective fusion module and the multi-dimensional feature diffusion mechanism, significantly improving detection accuracy. Secondly, the feature enhancement aggregation module (FEAM) is constructed in the backbone network, which optimizes the feature extraction process by integrating the information from different scale sensory fields and enhances the network??s enhancement and aggregation capability of multi-scale features, thus improving the detection of dense student groups. Finally, the traditional detection head is improved to a dynamic detection head (DyHead), effectively improving occluded students?? recognition and reducing misdetection and omission by enhancing the multi-scale perception ability. Experimental results show that, on the POCO dataset, MFD-YOLO improves mAP0.50 and mAP0.50:0.95 by 4.2 and 6.0 percentage points, respectively, compared with the base model YOLO11n, which significantly improves the detection accuracy and effectively reduces the false and missed detection rates. On the SCB-Dataset3 dataset, MFD-YOLO improves mAP0.50 and mAP0.50:0.95 by 3.4 and 4.4 percentage points, respectively, which further validates the applicability and robustness of the improved algorithm and proves its potential application in classroom behavior detection.

Key words: classroom behavior detection, high accuracy, YOLO11, multi-dimensional feature flow network (MFFN), feature enhancement aggregation module (FEAM)