计算机工程与应用2024，Vol.60Issue(11)：251-257,7.DOI:10.3778/j.issn.1002-8331.2304-0352
改进YOLOv5的智慧课堂人脸检测算法
Intelligent Classroom Face Detection Algorithm with Improved YOLOv5
摘要
Abstract
The intelligent classroom is a popular application scenario in the field of artificial intelligence.This paper pro-poses a face detection algorithm based on improved YOLOv5,named YOLOv5-SASA,to address the issues of missed or false detection caused by small or occluded faces in images captured by cameras located far away or at an angle.The algo-rithm consists of three parts.Firstly,the CSPDarknet53 network is utilized in the backbone layer,and the BasicRFB module is used in the final spatial pooling layer to enhance the network's feature extraction ability.Secondly,the NWD loss function is employed to improve the model's robustness in detecting small targets.Thirdly,the independent self-attention mecha-nism module SASA is introduced in the head layer to address the issue of face occlusion and reduce the model's param-eter count.Finally,the improved YOLOv5 network is optimized by reducing the number of neurons in the middle layer channels and adjusting the learning rate to avoid overfitting.Experimental results demonstrate that the proposed method outperforms the original network in the easy,medium,and hard levels of the WiderFace validation set,achieving accuracies of 97.5%,96.3%,and 86.5%,respectively,which effectively improves the accuracy of face detection in classroom scenarios.关键词
智慧课堂/人脸检测/YOLOv5/独立自注意力机制Key words
smart classroom/face detection/YOLOv5/stand-alone self-attention分类
信息技术与安全科学引用本文复制引用
钟源,袁家政,李鸿天,刘宏哲,徐成..改进YOLOv5的智慧课堂人脸检测算法[J].计算机工程与应用,2024,60(11):251-257,7.基金项目
国家自然科学基金(62171042,62102033,62006020) （62171042,62102033,62006020）
北京市重点科技项目(KZ202211417048) （KZ202211417048）
北京市属高等学校高水平科研创新团队建设支持计划项目(BPHR20220121) （BPHR20220121）
北京市自然科学基金(4232026) （4232026）
协同创新中心(CYXC2203). （CYXC2203）
