<![CDATA[Quality control and assurance in pharmaceutical vial manufacturing are paramount to ensure drug safety and efficacy. Defects such as cracks, bubbles, black spots, and wrinkles can compromise product quality and patient safety. This study proposes a novel methodology that integrates fast non-local means (FNLM) filtering with hybrid image processing techniques to detect these defects. Previous approaches have often struggled with subtle anomalies in texture and surface features. The proposed solution leverages phase structure analysis, utilizing phase stretch transform (PST) to effectively highlight subtle anomalies by extracting features sensitive to phase variations. These features are further refined using Gaussian filtering, with Otsu thresholding applied for precise segmentation and defect boundary identification. Morphological dilation enhances detection speed and accuracy, while region of interest (ROI) identification aids in localizing defects and facilitating decision-making. The system demonstrates significant improvements in quality control, achieving high performance metrics: precision (98.85%), recall (98.57%), accuracy (98.36%), specificity (98.0%), and F1-score (98.71%). It also achieves impressive AUC-ROC (98.18%) and AUC-PR (99.08%) values, demonstrating its robustness and suitability for defect detection in pharmaceutical vials.]]>
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