计算机与数字工程2024，Vol.52Issue(1)：162-168,7.DOI:10.3969/j.issn.1672-9722.2024.01.026
基于嵌入式终端的YOLOv3算法优化实现
Optimized Rearealization of YOLOv3 Algorithm Based on Embedded Terminal
摘要
Abstract
Image object recognition technology is a hot issue in the field of computer vision research.However,most of the cur-rent advanced object detection algorithms are based on server-side training and deployment.Under the background of today's mobile Internet era,they cannot be truly applied.At the same time,taking into account the needs of localized chips and software develop-ment environment,the YOLOv3 detection model is optimized and trained,and the model is deployed based on the embedded termi-nal,the Baidu EdgeBoard Edge AI Computing Box.Results of experiment fully show that the optimized YOLOv3-MobileNetv1 mod-el has a good detection and recognition effect on pedestrians,vehicles,airplanes and other types of objects.关键词
嵌入式终端/目标检测/深度学习/轻量化模型Key words
embedded terminal/object detection/deep learning/lightweight model分类
天文与地球科学引用本文复制引用
侯勇,杨争争,薛少辉,翟二宁..基于嵌入式终端的YOLOv3算法优化实现[J].计算机与数字工程,2024,52(1):162-168,7.基金项目
装备预研领域基金项目(编号:61403120205)资助. （编号:61403120205）
