检索结果-南通市图书馆

Industrial Fusion cascade detection of Solder Joint

Computers, Materials and Continua 2024年第1期81卷 1197-1214页

作者： Li C. Zhang P. Wang S. Liu L. Shi M. Chongqing School University of Chinese Academy of Sciences Chongqing University Chongqing 400044 China Automatic Reasoning and Cognition Research Center Chongqing Institute ofGreen and Intelligent Technology Chinese Academy of Sciences Chongqing 400714 China College of Artificial Intelligence Chongqing School University of Chinese Academy of Sciences Chongqing 400714 China Design Department Liuzhou Fuzhen Bodywork Industrial Co. Ltd. Liuzhou 545006 China

With the remarkable advancements in machine vision research and its ever-expanding applications, scholars have increasingly focused on harnessing various vision methodologieswithin the industrial realm. Specifically, detecting vehicle floor welding points poses unique challenges, including high operational costs and limited portability in practical settings. To address these challenges, this paper innovatively integrates template matching and the Faster RCNN algorithm, presenting an industrial fusion cascaded solder joint detection algorithm that seamlessly blends template matching with deep learning techniques. This algorithm meticulously weights and fuses the optimized features of both methodologies, enhancing the overall detection capabilities. Furthermore, it introduces an optimized multi-scale and multi-template matching approach, leveraging a diverse array of templates and image pyramid algorithms to bolster the accuracy and resilience of object detection. By integrating deep learning algorithms with this multi-scale and multi-template matching strategy, the cascaded target matching algorithm effectively accurately identifies solder joint types and positions. A comprehensive welding point dataset, labeled by experts specifically for vehicle detection, was constructed based on images from authentic industrial environments to validate the algorithm's performance. Experiments demonstrate the algorithm's compelling performance in industrial scenarios, outperforming the single-template matching algorithm by 21.3%, the multi-scale and multitemplate matching algorithm by 3.4%, the Faster RCNN algorithm by 19.7%, and the YOLOv9 algorithm by 17.3% in terms of solder joint detection accuracy. This optimized algorithm exhibits remarkable robustness and portability, ideally suited for detecting solder joints across diverse vehicle workpieces. Notably, this study's dataset and feature fusion approach can be a valuable resource for other algorithms seeking to enhance their

关键词： cascade object detection deep learning feature fusion multi-scale and multi-template matching solder joint dataset

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：