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This paper proposes a deep learning classification method for detecting the defects in pills which is the day to day necessity of a pharmaceutical industry. This paper uses a Fully Convolutional Data Description (FCDD) network which detects the pill detects and provides an enhanced control on quality measures. Detection of anomaly in pills using FCDD plays a crucial role in the pharmaceutical industry by its visual inspection process and automatic quality check. The ability of the proposed algorithm proves its validation in detecting the pill image anomalies paving its way in meeting effective safety and stringent standards adhering to customer health and upholding industry regulations. Thus, this paper contributes to the path of achieving Sustainable Development Goal-3, respectively.

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Detection of Pharmaceutical Pill Defects Through Deep One-Class Classification

  • 会议论文

  • 发表日期：

    2025年

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  • Kunal Roy Choudhury,
  • Animesh Singh,
  • S. Padmini

摘要

This paper proposes a deep learning classification method for detecting the defects in pills which is the day to day necessity of a pharmaceutical industry. This paper uses a Fully Convolutional Data Description (FCDD) network which detects the pill detects and provides an enhanced control on quality measures. Detection of anomaly in pills using FCDD plays a crucial role in the pharmaceutical industry by its visual inspection process and automatic quality check. The ability of the proposed algorithm proves its validation in detecting the pill image anomalies paving its way in meeting effective safety and stringent standards adhering to customer health and upholding industry regulations. Thus, this paper contributes to the path of achieving Sustainable Development Goal-3, respectively.