摘要
针对目标检测模型参数量大,难以部署在移动端设备的问题,提出了一种轻量化车辆行人检测模型YOLOv8-TI(traffic information)。设计全新的轻量级参数共享SPG Detect检测头以降低模型的参数量和计算量;提出全局平衡通道路径聚合网络(GBC-PAN)结构,平衡网络通道数量,通过跨尺度的加权链接,实现了自顶向下和自底向上的双向特征融合;此外,引入动态非单调聚焦机制的损失函数(Wise Loss)代替原损失函数以提升预测框精度。实验结果发现,提出的目标检测模型YOLOv8-TI在保持较高精度的同时,参数量、计算量和模型体积分别为YOLOv8n的52.1%、58.0%和54%。通过与其他轻量级目标检测模型对比,验证了该方法的有效性和卓越性。将YOLOv8-TI进行Android移动端部署,在荣耀20和荣耀80 GT上进行了测试,FPS可达24帧和31帧,满足实时性需求,有望进一步集成在自动驾驶汽车上完成交通信息检测功能。
Object detection models usually have a large number of parameters,making them inapplicable on mobile devices.Against this backdrop,we propose a lightweight vehicle and pedestrian detection model,YOLOv8-TI(Traffic Information).A novel lightweight parameter-sharing SPG Detect detection head is designed to reduce the model’s parameters and computational load.The Global Balanced Channel Path Aggregation Network(GBC-PAN)structure is proposed to balance the number of network channels and achieve bidirectional feature fusion from top-down and bottom-up directions through weighted connections across scales.Meanwhile,a dynamic non-monotonic focusing mechanism,represented by the Wise Loss function,is introduced to enhance the accuracy of predicted bounding boxes.Our experimental results reveal the YOLOv8-TI model maintains a high accuracy rate while reducing the parameters,flops,and model volume by 52.1%,58.0%and 54%respectively compared with those of YOLOv8n.A comparative analysis with other lightweight object detection algorithms verifies the effectiveness and superiority of our method.YOLOv8-TI is put on Android mobile devices and tested on Honor 20 fps and Honor 80GT,achieving frame rates of 24 and 31 FPS respectively,meeting real-time requirements.It is set to accomplish traffic information detection tasks when applied on autonomous driving vehicles.
作者
王道斌
李宸翔
严运兵
WANG Daobin;LI Chenxiang;YAN Yunbing(School of Automotive and Traffic Engineering,Wuhan University of Science and Technology,Wuhan 430065,China)
出处
《重庆理工大学学报（自然科学）》
CAS
北大核心
2024年第11期35-42,共8页
Journal of Chongqing University of Technology：Natural Science
基金
国家自然科学基金项目(51975428)。
关键词
深度学习
车辆行人检测
参数共享
轻量化
deep learning
car and pedestrian detection
sharing Parameter
light weight
作者简介
王道斌,男,博士,副教授,主要从事交通信息采集研究,E-mail:wangdaobin05@163.com;通信作者:李宸翔,男,硕士研究生,主要从事交通信息检测和计算机视觉研究,E-mail:837238173@qq.com;严运兵,男,博士,教授,主要从事智能驾驶人机交互与决策和新能源汽车驱动与控制研究。
