  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.
