[1]姜越夫,王 青,吕绪山.改进 YOLOv5s 的纱管目标检测方法[J].机械与电子,2024,42(02):29-34.
　JIANG Yuefu,WANG Qing,LYU Xushan.Improved YOLOv5s Method for Yarn Tube Object Detection[J].Machinery & Electronics,2024,42(02):29-34.
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改进 YOLOv5s 的纱管目标检测方法()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
42
期数:
2024年02期
页码:
29-34
栏目:
自动控制与检测
出版日期:
2024-02-27

文章信息/Info

Title:
Improved YOLOv5s Method for Yarn Tube Object Detection
文章编号:
1001-2257 ( 2024 ) 02-0029-06
作者:
姜越夫; 王 青; 吕绪山
西安工程大学机电工程学院,陕西 西安 710699
Author(s):
JIANG Yuefu ;  WANG Qing ;  LYU Xushan
( School of Mechanical and Electrical Engineering , Xi ’an Polytechnic University , Xi ’an 710699 , China )
关键词:
深度学习; 纱管检测;  WIoU
Keywords:
deep learning ;  yarn tube detection ;  WIoU
分类号:
P391.41
文献标志码:
A
摘要:
为解决传统纱管分类方法效率低下、误差较高的问题,提出一种基于改进 YOLOv5s 算法的纱管目标识别方法。该算法融合了坐标注意力模块( CA )和 Transformer 模块,提出了新的 SPP 模块( SPP+ )替换传统的 SPP 模块,使用加权双向特征金字塔网络( BiFPN )思想增强特征融合,并使用 WIoU 损失函数替代原有的损失函数。为验证改进算法性能,制作了一套纱管数据集,并基于改进 YOLOv5s 算法进行了纱管检测实验。实验结果表明改进的算法具有更好的识别效果。
Abstract:
To address the issues of low efficiency and high errors in traditional yarn tube classification methods , a yarn tube object recognition method based on an improved YOLOv5s algorithm is proposed. The enhanced YOLOv5s algorithm integrates a coordinate attention ( CA ) module and a Transformer module , introduces a new spatial pyramid pooling plus ( SPP+ ) module to replace the conventional spatial pyramid pooling ( SPP ) module , enhances feature fusion using the weighted bidirectional feature pyramid network ( BiFPN ) concept , and replaces the original loss function with the wise intersection over Union ( WIoU ) loss function.To validate the performance of the improved algorithm , a yarn tube dataset is created , and yarn tube detection experiments are conducted based on the improved YOLOv5s algorithm.The experimental results show that the improved algorithm exhibits superior recognition capabilities.

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备注/Memo

备注/Memo:
收稿日期: 2023-08-31
基金项目:中国纺织工业联合会科技指导性项目( 2021019 )
作者简介:姜越夫 ( 1998- ),男,山西太原人,硕士,研究方向为计算机视觉;王 青 ( 1985- ),女,陕西西安人,讲师,硕士研究生导师,研究方向为三维视觉下的位姿估计。
更新日期/Last Update: 2024-03-22