Anomaly detection in manufacturing pipelines remains a critical challenge, intensified by the complexity and variability of industrial environments. This paper introduces AssemAI, an interpretable image-based anomaly detection system tailored for smart manufacturing pipelines. Our primary contributions include the creation of a tailored image dataset and the development of a custom object detection model, YOLO-FF, designed explicitly for anomaly detection in manufacturing assembly environments. Utilizing the preprocessed image dataset derived from an industry-focused rocket assembly pipeline, we address the challenge of imbalanced image data and demonstrate the importance of image-based methods in anomaly detection. The proposed approach leverages domain knowledge in data preparation, model development and reasoning. We compare our method against several baselines, including simple CNN and custom Visual Transformer (ViT) models, showcasing the effectiveness of our custom data preparation and pretrained CNN integration. Additionally, we incorporate explainability techniques at both user and model levels, utilizing ontology for user-friendly explanations and SCORE-CAM for in-depth feature and model analysis. Finally, the model was also deployed in a real-time setting. Our results include ablation studies on the baselines, providing a comprehensive evaluation of the proposed system. This work highlights the broader impact of advanced image-based anomaly detection in enhancing the reliability and efficiency of smart manufacturing processes.

翻译：制造流水线中的异常检测仍然是一个关键挑战，工业环境的复杂性和多变性加剧了这一难题。本文介绍了AssemAI，一种专为智能制造流水线设计的可解释图像异常检测系统。我们的主要贡献包括创建了一个定制化的图像数据集，以及开发了一个专门用于制造装配环境异常检测的定制目标检测模型YOLO-FF。利用源自工业级火箭装配流水线的预处理图像数据集，我们解决了图像数据不平衡的挑战，并论证了基于图像的方法在异常检测中的重要性。所提出的方法在数据准备、模型开发和推理过程中充分运用了领域知识。我们将本方法与多种基线模型进行了比较，包括简单的CNN和定制的视觉Transformer（ViT）模型，展示了我们定制化数据准备和预训练CNN集成的有效性。此外，我们在用户层面和模型层面均引入了可解释性技术：利用本体论提供用户友好的解释，并采用SCORE-CAM进行深入的特征和模型分析。最终，该模型还部署于实时环境中。我们的结果包括对基线模型的消融研究，为所提系统提供了全面评估。本工作凸显了先进图像异常检测技术在提升智能制造过程可靠性与效率方面的广泛影响。