高技术通讯2024，Vol.34Issue(4)：356-365,10.DOI:10.3772/j.issn.1002-0470.2024.04.003
针对嵌入式设备的YOLO目标检测算法改进方法
Improvement methods for YOLO object detection algorithm targeting embedded devices
摘要
Abstract
To address the problem of implementing algorithms on resource-limited embedded devices,a lightweight im-provement is proposed based on the YOLO series of algorithms to adapt to embedded device implementation,specif-ically including:improving the network backbone by introducing GhostNet ideas based on the YOLOv4-Tiny algo-rithm structure to significantly reduce network parameters and computational complexity;strengthening the fusion effect of neck network features to reduce accuracy loss caused by model compression;and using quantization during training to convert network model parameters from 32-bit floating-point data to 8-bit fixed-point parameters suitable for embedded device computation.Experimental results show that after the improvement in this paper,the network's model size relative to the original algorithm is reduced by 57％when the detection accuracy meets application re-quirements,and the power consumption for embedded device implementation is only 3.795 W.关键词
目标检测/YOLOv4-Tiny/轻量化设计/嵌入式实现/加速器Key words
object detection/YOLOv4-Tiny/lightweight design/embedded implementation/accelerator引用本文复制引用
张立国,孟子杰,金梅..针对嵌入式设备的YOLO目标检测算法改进方法[J].高技术通讯,2024,34(4):356-365,10.基金项目
国家重点研发计划(2020YFB1711001)资助项目. （2020YFB1711001）
