检索结果-内蒙古大学图书馆

6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Chen, Yuejun Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China

ISBN: (纸本)9798350305623

This investigation is dedicated to developing an advanced ceramic tile defect detection system based on YOLOv5 and MobileNetV3, aimed at augmenting the efficiency and precision of quality control in industrial tile production. The research is grounded in the pressing need for automated and precise technologies in the field of industrial quality inspection. Initially, the paper outlines the potential of deep learning algorithms in detecting flaws in ceramic tiles and identifies YOLOv5 and MobileNetV3 as the pivotal models. The methodology encompasses the integration of these models and their specific optimization for tile defect detection tasks. Key findings indicate that the amalgamation of these models significantly enhances detection efficiency and accuracy, particularly in resource-constrained edge computing environments. Moreover, the study underscores the potential applications of deep learning in industrial automation, providing valuable data and insights for future research. The unique contribution of this research lies in its innovative exploration of real-time, efficient methods for detecting defects in ceramic tiles. © 2023 IEEE.

关键词： ceramic tile defect detection Deep Learning Industrial Automation MobileNetV3 YOLOv5

来源：评论

学校读者我要写书评

暂无评论

A supervised approach for automated surface defect detection in ceramic tile quality control

引用

ADVANCED ENGINEERING INFORMATICS 2022年第0期53卷

作者： Lu, Qinghua Lin, Junmeng Luo, Lufeng Zhang, Yunzhi Zhu, Wenbo Foshan Univ Sch Mechatron Engn & Automat Foshan 528000 Peoples R China

Surface defect detection is very important to guarantee the quality of ceramic tiles production. At present, this process is usually performed manually in the ceramic tile industry, which is low efficiency and time-consuming. For small surface defects detection of high-resolution ceramic tiles image, an intelligent detection method for surface defects of ceramic tiles based on an improved you only look once version 5 (YOLOv5) algorithm is presented. Firstly, the high-resolution ceramic tile images are cropped into slices, and the Bottleneck module in the YOLOv5s network is optimized by introducing depthwise convolution and replaced in the whole network. Then, feature extraction is performed using the improved Shufflenetv2 backbone, and an attention mechanism is added to the backbone network to improve the feature extraction ability. The path aggregation network (PAN) and Feature Pyramid Networks (FPN) neck are used to enhance the feature extraction, and finally, the YOLO head is used to identify and locate the ceramic tile defects. The multiple sliding windows detection method is proposed to detect the original ceramic tile image which is faster than the single sliding window detection method. The experimental results show that compared with the original YOLOv5s detection algorithm, the pa-rameters of the model are reduced by 20.46 %, the floating point operations are reduced by 26.22 %, and the mean average precision (mAP) of the proposed method is 96.73 % in the ceramic tile image slice test set which has 1.93 % improvement in mAP than the original YOLOv5s. Compare with other object detection methods, the method proposed in this paper also has certain advantages. In the high-resolution ceramic tile images test set, the mAP of the proposed algorithm is 86.44 % by using the multiple sliding window detection method. The ceramic defect detection experiment has verified the feasibility of the method proposed in this paper.

关键词： High resolution ceramic tile defect detection Deep learning YOLOv5

来源：评论

学校读者我要写书评

暂无评论

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

请选择保存的检索档案：

请选择收藏分类：

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

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

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：