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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
C. R., Vishwanatha
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Computer Science & IT Engineering

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Automated vial defect inspection using Gabor wavelets and k-means clustering C. R., Vishwanatha; Asha, V.; Channabasava, Channabasava; Rallapalli, Sreekanth
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4279-4289

Abstract

This study proposes a machine vision-based defect inspection system for pharmaceutical vials, aiming to ensure the quality and safety of medicinal fluids. The system employs a series of image processing techniques, including denoising, feature extraction using the Gabor wavelet transform, segmentation, clustering with the K-means algorithm, and precise defect identification using the Canny edge operator. Experimental results demonstrate high performance, with recall, precision, accuracy, and F1-score exceeding 98%. Additionally, the proposed method achieves area under the curve-receiver-operating characteristic curve (AUC-ROC) and AUC-precision-recall (PR) values of approximately 98%. The system's average computational time is 355 microseconds, indicating its potential for real-time defect detection. Overall, this approach offers an effective solution for identifying various cosmetic defects such as scratches, bruises, cracks, and black spots, in pharmaceutical vials without the need for vial classification training. 
Co-Authors Asha, V. Channabasava, Channabasava Rallapalli, Sreekanth