用视觉变压器自动检测金属表面缺陷，提升制造效率。
Automated Detection of Defects on Metal Surfaces using Vision Transformers
- 基于视觉变压器提取特征，双路径分工分类与定位缺陷。
- 分类准确率高，定位误差（MSE/MAE）极低。
- 适合工业质检场景，替代人工检查提高精度。
金属制造常产生缺陷品，导致运营挑战。传统人工检测耗时且资源密集，亟需自动化解决方案。本研究采用深度学习技术，构建基于视觉变压器（ViT）的金属表面缺陷检测模型，聚焦缺陷的分类与定位。模型架构分为两条分支：分类路径与定位路径。目标是在保证高分类准确率的同时，将定位过程中的均方误差（MSE）和平均绝对误差（MAE）降至最低。实验结果表明，该模型可有效应用于自动化缺陷检测流程，显著提升生产效率并减少制造错误。
原文摘要 · Abstract (English)
Metal manufacturing often results in the production of defective products, leading to operational challenges. Since traditional manual inspection is time-consuming and resource-intensive, automatic solutions are needed. The study utilizes deep learning techniques to develop a model for detecting metal surface defects using Vision Transformers (ViTs). The proposed model focuses on the classification and localization of defects using a ViT for feature extraction. The architecture branches into two paths: classification and localization. The model must approach high classification accuracy while keeping the Mean Square Error (MSE) and Mean Absolute Error (MAE) as low as possible in the localization process. Experimental results show that it can be utilized in the process of automated defects detection, improve operational efficiency, and reduce errors in metal manufacturing.
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