现代电子技术2024，Vol.47Issue(15)：133-138,6.DOI:10.16652/j.issn.1004-373x.2024.15.022
基于改进YOLOv8s的多尺度检测算法
Multi-scale object detection algorithm based on improved YOLOv8s
文思予 1张上 2张朝阳 2冉秀康2
作者信息
- 1. 重庆理工大学,重庆 400054||水电工程智能视觉监测湖北省重点实验室,湖北 宜昌 443002||湖北省建筑质量检测装备工程技术研究中心,湖北 宜昌 443002
- 2. 水电工程智能视觉监测湖北省重点实验室,湖北 宜昌 443002||湖北省建筑质量检测装备工程技术研究中心,湖北 宜昌 443002
- 折叠
摘要
Abstract
In view of the low detection accuracy caused by insufficient feature information of small objects like insulators and the large model size that is not conducive to hardware transplantation,a multi-scale detection algorithm MPH-YOLO(multiple prediction head-you only look once)is proposed.The algorithm MPH-YOLO improves the detection ability of small objects by expanding the detection scale of small object first,and then the CIoU loss function in YOLOv8s is replaced with the SIoU loss function as the edge loss function,so as to enhance the positional accuracy of the object.The Ghost convolution with lower cost is introduced instead of the traditional convolution in the network structure,so as to lighten the volume of the model.In terms of the improved algorithm,both its detection accuracy and model lightweight on the insulator dataset are improved,with a detection accuracy of 86.2%for mAP50-95 and a model volume of only 4.7 MB.The experimental results show that the MPH-YOLO not only can improve the detection of small objects effectively,but also be more lightweight for hardware transplantation,so it has high practical value.关键词
绝缘子/多尺度检测/小目标/YOLOv8s/SIoU/Ghost卷积Key words
insulator/multi-scale detection/small object/YOLOv8s/SIoU/Ghost convolution分类
信息技术与安全科学引用本文复制引用
文思予,张上,张朝阳,冉秀康..基于改进YOLOv8s的多尺度检测算法[J].现代电子技术,2024,47(15):133-138,6.
