基于改进YOLOv8s模型的电动车骑乘人员头盔佩戴检测OA
Detection of Helmet Wearing of Electric Bicycle Riders Based on Improved YOLOv8s Model
针对电动车头盔检测模型易受天气、视角等因素影响,存在漏检、误检、精度低和实时检测效率差等问题,基于原始YOLO第 8 小版(you only look once version 8 small,YOLOv8s)模型进行改进,提出改进YOLOv8s模型.主干特征提取网络选用轻量级的香草网络(vanilla network,VanillaNet)模块,颈部网络采用轻量级的上采样算子内容感知特征重组(content-aware reassembly o…查看全部>>
To address the issues of missed,false detection,low precision,and poor efficiency of real-time detection in electric bicycle helmet detection models caused by factors such as weather and viewing angles,an improved you only look once version 8 small(YOLOv8s)model was proposed based on original YOLOv8s model.The lightweight vanilla network(VanillaNet)module was selected for the backbone feature extraction network,and the lightweight upsampling operator content…查看全部>>
袁宇乐;汤文兵
安徽理工大学 计算机科学与工程学院,安徽 淮南 232001安徽理工大学 计算机科学与工程学院,安徽 淮南 232001
计算机与自动化
深度学习目标检测YOLOv8sVanillaNetCARAFE极小目标检测层MPDIoU
deep learningobject detectionYOLOv8sVanillaNetCARAFEtiny object detection layerMPDIoU
《湖北民族大学学报（自然科学版）》 2024 (3)
355-360,367,7
国家自然科学基金项目(52374154).
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