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<p>This paper proposes a data-driven methodology within the “detect-repair” strategy of zero-defect manufacturing (ZDM) for quality assessment of tires at the end of the production line. By integrating a decision support system (DSS) based on multiple instance learning (MIL) with an existing defect detection system, our approach marks a significant step forward in automating accurate tire quality assessment. This enables informed decisions regarding repair or delivery to be made without manual intervention. Our proposed method extends its utility across any manufacturing sectors needing to ensure its product quality based on tacit knowledge. Notably, the proposed DSS leverages historical data to outperform rule-based systems through an attention-based deep learning neural network, improving decision-making accuracy and reducing false positives. Moreover, the attention scores are used to obtain explainable predictions for each detected defect. The achieved area under the curve (AUC) value of 0.837 demonstrates the proposed system’s strong predictive performance. This innovative data-driven methodology not only represents progress in ZDM but also allows to make a step further towards more efficient, adaptive decision support systems in industrial quality control.</p>

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From detections to decisions: an attention-guided deep multiple instance learning approach for visual tire inspection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月6日

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  • Thomas Mignot,
  • François Ponchon,
  • Alexandre Derville,
  • Stefan Duffner,
  • Christophe Garcia

摘要

This paper proposes a data-driven methodology within the “detect-repair” strategy of zero-defect manufacturing (ZDM) for quality assessment of tires at the end of the production line. By integrating a decision support system (DSS) based on multiple instance learning (MIL) with an existing defect detection system, our approach marks a significant step forward in automating accurate tire quality assessment. This enables informed decisions regarding repair or delivery to be made without manual intervention. Our proposed method extends its utility across any manufacturing sectors needing to ensure its product quality based on tacit knowledge. Notably, the proposed DSS leverages historical data to outperform rule-based systems through an attention-based deep learning neural network, improving decision-making accuracy and reducing false positives. Moreover, the attention scores are used to obtain explainable predictions for each detected defect. The achieved area under the curve (AUC) value of 0.837 demonstrates the proposed system’s strong predictive performance. This innovative data-driven methodology not only represents progress in ZDM but also allows to make a step further towards more efficient, adaptive decision support systems in industrial quality control.