基于多维度动态衰减Transformer的轮胎检测算法应用
Application of tire detection algorithm based on multi-dimensional dynamic attenuation Transformer
朱焕宇 1王明泉 1贾虎 1商奥雪 1谢绍鹏1
作者信息
- 1. 中北大学信息与通信工程学院 太原 030051
- 折叠
摘要
针对当前国内子午线轮胎缺陷分割困难、成本昂贵的问题,本文提出了如下解决方案,提出一种基于Swin Transformer和注意力特征金字塔的子午线轮胎缺陷分割算法Swin DAA,其中主要使用Swin Transformer作为主干特征提取网络,经过动态衰减注意力特征金字塔增强语义表达能力,搭建由Python语言编写的软件平台,同时级联X射线载重轮胎检测系统采集图像,并且使用TCP协议与上位机通信、传输图像数据,最终将缺陷分割软件系统与MES工控系统连接,完成无人监守的自动化子午线轮胎缺陷分割.实验对比数据显示,本文中提出的Swin DAA网络的精准度达到了 82.87％,召回率达到了 85.22％,每秒传输帧数达到了 11,所集成的软件能良好的完成子午线轮胎的实际监测要求.
Abstract
In response to the current difficulties and high costs in segmenting radial tire defects in China,this paper proposes the following solution:a radial tire defect segmentation algorithm called Swin DA A based on Swin Transformer and attention feature pyramid.Swin Transformer is mainly used as the backbone feature extraction network,and the semantic expression ability is enhanced through the Dynamic Attenuation Attention feature pyramid,Build a software platform written in Python language,cascade the X-ray heavy-duty tire detection system to collect images,and use TCP protocol to communicate with the upper computer and transmit image data.Finally,connect the defect segmentation software system with the MES industrial control system to complete unmanned automated radial tire defect segmentation.The experimental comparison data shows that the Swin DAA network proposed in this article has an accuracy of 82.87％,a recall rate of 85.22％,and a transmission frame rate of 11 per second.The integrated software can effectively meet the actual monitoring requirements of radial tires.
关键词
动态衰减/编解码器/深度学习/缺陷分割/子午线轮胎Key words
dynamic attenuation attention/transformer/deep learning/defect segmentation/radial tire引用本文复制引用
基金项目
山西省重点研发计划(201803D121069)
山西省高等学校科技创新项目(2020L0624)
山西省信息探测与处理重点实验室基金(ISPT2020-5)
国家自然科学基金(61171177)
国家重大科学仪器设备开发专项(2013YQ240803)
出版年
2024
NETL
NSTL
万方数据