Development of a Defect Diagnosis Algorithm for Blow-molded Transparent Plastic Bottles based on Convolutional Neural Networks (CNN)

Abstract

Polyethylene terephthalate (PET) bottles are widely used for food and beverage packaging, produced through blow-molding processes. Defects in PET bottles often arise due to improper process conditions, with visual inspections commonly performed by workers. However, the growing importance of quality assurance in consumer goods has driven demand for advanced defect detection methods using image processing or deep learning. This study develops a defect diagnosis algorithm for transparent PET bottles based on a convolutional neural network (CNN). A testbed was created to capture images of PET bottles, collecting datasets of normal and defective bottles using a vision camera. An image processing sequence was designed to enhance feature extraction of defective areas during CNN computations. The CNN-based model was trained to classify normal and defective bottles and optimized using grid search to select the model with the highest accuracy.Results demonstrate significant improvements in diagnosis accuracy when the proposed image processing technique is applied to the training and testing datasets. This study provides a robust framework for automating defect detection in PET bottles, highlighting the potential of combining CNN with enhanced preprocessing techniques for quality assurance in manufacturing.