智能系统学报2025，Vol.20Issue(4)：871-881,11.DOI:10.11992/tis.202406015
面向边缘设备的目标检测模型研究
Research on object detection models for edge devices
摘要
Abstract
Existing object detection models can be improved in terms of balancing detection performance and inference speed on edge devices.Hence,a YOLO(you can only look once)v8-based model optimized for various edge devices is proposed.In the Backbone,an EC2f(extended coarse-to-fine)structure is designed to reduce parameters,computation,and data read/write volume.In the Neck,the YOLOv6-3.0 version is used to accelerate inference while maintaining ac-curacy.In the Head,a multiscale convolutional detection head,which further reduces computational load and complex-ity,is featured.Two versions(n/s scales)are designed to suit different edge devices.Experiments on an X-ray dataset demonstrate that the proposed model improves inference accuracy by 0.5％/1.7％and speed by 11.6％/11.2％compared with baseline models of the same scale.Generalization tests on other datasets present an increase in inference speed of over 10％and an accuracy reduction controlled within 1.3％.Overall,the model achieves a satisfactory balance between inference accuracy and speed.关键词
目标检测/YOLO/边缘设备/推理精度/推理速度/数据读写量/计算复杂度/模型部署Key words
object detection/YOLO/edge devices/inference accuracy/inference speed/data read/write volume/compu-tational load/model deployment分类
信息技术与安全科学引用本文复制引用
徐伟峰,雷耀,王洪涛,张旭..面向边缘设备的目标检测模型研究[J].智能系统学报,2025,20(4):871-881,11.基金项目
国家自然科学基金项目(61802124) （61802124）
中央高校基本科研业务费专项(2023MS137) （2023MS137）
中国高校产学研创新基金项目(2023DT6). （2023DT6）
