AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines
<|reference_start|>AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines: 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.<|reference_end|>
@article{prasad2024assemai:,
    title={AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing
  Pipelines},
    author={Renjith Prasad, Chathurangi Shyalika, Ramtin Zand, Fadi El Kalach,
  Revathy Venkataramanan, Ramy Harik, Amit Sheth},
    journal={Predictive Models in Engineering Applications special session
  (MLPMEA) at International Conference on Machine Learning and Applications
  (ICMLA) 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02181},
    primaryClass={cs.CV}
}