计算机科学与探索2025，Vol.19Issue(8)：2135-2148,14.DOI:10.3778/j.issn.1673-9418.2501045
改进YOLO11的高精度课堂行为检测算法
Improved YOLO11 Algorithm for Highly Accurate Classroom Behavior Detection
摘要
Abstract
Aiming at the problem that student targets in classroom scenes are small,densely distributed,and easily ob-scured,resulting in low detection accuracy and poor recognition effect,an improved classroom behavior detection algo-rithm based on YOLO11(you only look once version 11),named MFD-YOLO,is proposed.Through a series of innova-tive 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 tar-gets 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 im-proves 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 vali-dates the applicability and robustness of the improved algorithm and proves its potential application in classroom behavior detection.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)Key words
classroom behavior detection/high accuracy/YOLO11/multi-dimensional feature flow network(MFFN)/feature enhancement aggregation module(FEAM)分类
信息技术与安全科学引用本文复制引用
曹燚,曹倩,钱承山,袁程胜..改进YOLO11的高精度课堂行为检测算法[J].计算机科学与探索,2025,19(8):2135-2148,14.基金项目
无锡市"太湖之光"科技攻关(基础研究)项目(K20241046) （基础研究）
国家传感网工程技术研究中心开放课题(2024YJZXKFKT02) （2024YJZXKFKT02）
国家自然科学基金(62102189,62122032) （62102189,62122032）
江苏高校哲学社会科学研究一般项目(2023SJYB0919) （2023SJYB0919）
无锡学院引进人才科研启动专项经费(2022r043).This work was supported by the"Taihu Light"Science and Technology Project of Wuxi(K20241046),the Open Project of National Engineering Technology Research Center for Sensor Network(2024YJZXKFKT02),the National Natural Science Foundation of China(62102189,62122032),the Jiangsu Universities General Project for Philosophy and Social Science Research(2023SJYB0919),and the Special Fund for the Introduction of Talent Research of Wuxi University(2022r043). （2022r043）
