标题： 基于噪声融合的蒸馏学习在复杂工业环境中的异常检测 显示英文标题

标题： Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environments

摘要： 工业制造中的异常检测和定位可以显著提高生产效率和产品质量。现有方法能够在预定义或受控的成像环境中检测表面缺陷。然而，在复杂且无结构的工业环境中，准确地检测工件缺陷（具有不同的视角、姿态和光照条件）仍然具有挑战性。我们提出了一种专门设计用于处理具有扰动模式输入的新颖异常检测和定位方法。我们的方法引入了一个基于协作蒸馏异构教师网络（HetNet）、自适应局部-全局特征融合模块以及局部多变量高斯噪声生成模块的新框架。 HetNet能够利用有限的关于局部干扰变化的信息来学习建模正常模式下的复杂特征分布。我们在主流基准数据集上进行了广泛的实验。HetNet在工业条件下的MSC-AD数据集上展示了卓越的性能，所有评估指标均提升了大约10%，同时在其他数据集上达到了最先进的结果，验证了其对环境波动的鲁棒性及其增强工业异常检测系统在多样化场景下可靠性的能力。真实环境中的测试进一步确认HetNet可以有效地集成到生产线上以实现稳健且实时的异常检测。 代码、图像和视频已发布在项目网站：https://zihuatanejoyu.github.io/HetNet/ 显示英文摘要

摘要： 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/