针对复杂工业环境下的缺陷检测难题，提出新型噪声融合蒸馏方法。
Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environments
- 构建异构教师网络与局部全局特征融合机制
- 在MSC-AD上各项指标提升约10%，优于现有方法
- 适合部署于真实产线的实时缺陷检测系统
自动化工业制造中的异常检测与定位可显著提升生产效率和产品质量。现有方法在预设或受控成像环境下能有效检测表面缺陷，但在视角、姿态和光照变化较大的复杂非结构化工业环境中准确检测工件缺陷仍具挑战。本文提出一种专为含扰动模式输入设计的新颖异常检测与定位方法。该方法引入基于协同蒸馏的异构教师网络（HetNet）、自适应局部-全局特征融合模块及局部多变量高斯噪声生成模块。HetNet仅需少量局部扰动信息即可学习正常模式的复杂特征分布。在主流基准上的大量实验表明，该方法在工业条件下对MSC-AD数据集的各类评估指标均实现约10%的提升，同时在其他数据集上达到最先进水平，验证了其对环境波动的鲁棒性以及增强工业异常检测系统可靠性的能力。实际场景测试进一步证实HetNet可有效集成至产线，实现稳定且实时的异常检测。代码、图像与视频已发布于项目网站：https://zihuatanejoyu.github.io/HetNet/
原文摘要 · Abstract (English)
Anomaly detection and localization in automated industrial manufacturing can significantly enhance production efficiency and product quality. Existing methods are capable of detecting surface defects in pre-defined or controlled imaging environments. However, accurately detecting workpiece defects in complex and unstructured industrial environments with varying views, poses and illumination remains challenging. We propose a novel anomaly detection and localization method specifically designed to handle inputs with perturbative patterns. Our approach introduces a new framework based on a collaborative distillation heterogeneous teacher network (HetNet), an adaptive local-global feature fusion module, and a local multivariate Gaussian noise generation module. HetNet can learn to model the complex feature distribution of normal patterns using limited information about local disruptive changes. We conducted extensive experiments on mainstream benchmarks. HetNet demonstrates superior performance with approximately 10% improvement across all evaluation metrics on MSC-AD under industrial conditions, while achieving state-of-the-art results on other datasets, validating its resilience to environmental fluctuations and its capability to enhance the reliability of industrial anomaly detection systems across diverse scenarios. Tests in real-world environments further confirm that HetNet can be effectively integrated into production lines to achieve robust and real-time anomaly detection. Codes, images and videos are published on the project website at: https://zihuatanejoyu.github.io/HetNet/
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