武汉工程大学学报2025，Vol.47Issue(5)：539-547,555,10.DOI:10.19843/j.cnki.CN42-1779/TQ.202409022
基于视觉变换器动态优化的瓷砖瑕疵检测模型
A ceramic tile defect detection model based on dynamic optimization of vision transformer
摘要
Abstract
Ceramic tile surface defects are characterized by their small size,irregular shapes,varying scales,and the challenge of differentiating them from complex patterned backgrounds,rendering high-precision defect detection extremely difficult.To address this issue,we proposed a high-performance ceramic tile surface defect detection model that seamlessly integrates a vision transformer(ViT)network with the efficient YOLOv8n architecture,achieving a favorable balance between accuracy and computational efficiency.First,an efficient ViT network was adopted as the backbone to enhance the model's capability for joint modeling of global image context and local features while maintaining computational efficiency.Then,space-to-depth convolution replaced standard downsampling convolution to preserve more fine-grained information.And a spatial-channel reorganization convolution module was incorporated at the end of the neck network to focus on critical feature information.Finally,a dynamic head mechanism was introduced to dynamically adjust attention weights,significantly improving the model's ability to detect complex defect features.Experiments on the Alibaba Cloud Tianchi ceramic tile defect detection dataset demonstrated that this optimized model achieved a mean average precision(mAP)of 71.5%,outperforming the original YOLOv8n by 11.6%.The proposed model excels in detecting tiny and densely distributed defects on tile surfaces while maintaining low deployment costs,fulfilling real-time quality inspection requirements in industrial production.关键词
深度学习/缺陷检测/YOLOv8n/视觉变换器/自注意力机制Key words
deep learning/defect detection/YOLOv8n/vision transformer/self-attention mechanism分类
信息技术与安全科学引用本文复制引用
罗芷萱,田斌,黎曦..基于视觉变换器动态优化的瓷砖瑕疵检测模型[J].武汉工程大学学报,2025,47(5):539-547,555,10.基金项目
国家自然科学基金(62367006) （62367006）
