Paper ID: 2408.02181 • Published Aug 5, 2024
AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines
Renjith Prasad, Chathurangi Shyalika, Ramtin Zand, Fadi El Kalach, Revathy Venkataramanan, Ramy Harik, Amit Sheth
TL;DR
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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. Utilizing a curated image dataset
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. Our primary contributions include deriving an image dataset,
fine-tuning an object detection model YOLO-FF, and implementing a custom
anomaly detection model for assembly pipelines. The proposed approach leverages
domain knowledge in data preparation, model development and reasoning. We
implement several anomaly detection models on the derived image dataset,
including a Convolutional Neural Network, Vision Transformer (ViT), and
pre-trained versions of these models. Additionally, we incorporate
explainability techniques at both user and model levels, utilizing ontology for
user-level explanations and SCORE-CAM for in-depth feature and model analysis.
Finally, the best-performing anomaly detection model and YOLO-FF are deployed
in a real-time setting. Our results include ablation studies on the baselines
and 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. The image dataset,
codes to reproduce the results and additional experiments are available at
this https URL