Abstract
We present an automated vision-based system for defect detection and classification of laser power meter sensor coatings. Our approach addresses the critical challenge of identifying coating defects such as thermal damage and scratches that can compromise laser energy measurement accuracy in medical and industrial applications. The system employs an unsupervised anomaly detection framework that trains exclusively on ``good'' sensor images to learn normal coating distribution patterns, enabling detection of both known and novel defect types without requiring extensive labeled defect datasets. Our methodology consists of three key components: (1) a robust preprocessing pipeline using Laplacian edge detection and K-means clustering to segment the area of interest, (2) synthetic data augmentation via StyleGAN2, and (3) a UFlow-based neural network architecture for multi-scale feature extraction and anomaly map generation. Experimental evaluation on 366 real sensor images demonstrates $93.8\%$ accuracy on defective samples and $89.3\%$ accuracy on good samples, with image-level AUROC of 0.957 and pixel-level AUROC of 0.961. The system provides potential annual cost savings through automated quality control and processing times of 0.5 seconds per image in on-device implementation.
Abstract (translated)
我们提出了一种基于视觉的自动化系统，用于检测和分类激光功率计传感器涂层中的缺陷。该方法旨在解决在医疗和工业应用中识别热损伤、划痕等可能导致激光能量测量精度降低的关键挑战。我们的系统采用了一个无监督异常检测框架，仅使用“良好”的传感器图像进行训练，以学习正常涂层分布模式，并能够不依赖大量标记的缺陷数据集来检测已知和新型缺陷类型。 该方法由三个关键组成部分构成：（1）一个稳健的预处理管道，利用拉普拉斯边缘检测和K-means聚类对感兴趣的区域进行分割；（2）通过StyleGAN2实现的合成数据增强；以及（3）基于UFlow的神经网络架构用于多尺度特征提取及异常图生成。 在包含366张真实传感器图像的数据集上，实验评估表明，在有缺陷样本上的准确率为93.8%，良好样本上的准确率为89.3%，整体AUROC值为0.957（图像级别）和0.961（像素级别）。该系统通过自动化的质量控制流程有望节省年度成本，并且在设备内实现时每张图片的处理时间仅为0.5秒。
URL
https://arxiv.org/abs/2509.20946
