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Optimised deep learning framework for glass defect detection using ResNet50V2 and Bayesian Optimisation
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AI
DOI：10.1080/14484846.2026.2669008.png)
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Abstract
En 中文
Ensuring high-quality production of glass products is essential in modern manufacturing, where even minor defects such as scratches, cracks, or surface irregularities can significantly affect safety, performance, and usability. Traditional visual inspection methods are labour-intensive, time-consuming, and prone to human error, creating a strong need for automated and reliable defect detection systems. The main objective of this research is to develop an efficient Deep Learning (DL) based framework for accurate binary classification of defective and non-defective glass products while addressing limitations such as subtle defect visibility, variation in lighting conditions, and dataset imbalance. Furthermore, the study aims to evaluate model performance under different hyperparameter settings using an intelligent optimisation technique. The proposed work employs the Residual Network 50 Version 2 (ResNet50V2) Convolutional Neural Network (CNN) for defect classification, supported by a comprehensive preprocessing pipeline consisting of resizing, normalisation, greyscale conversion, and data augmentation. Bayesian Optimisation (BO) is integrated to fine-tune key hyperparameters, ensuring optimal learning efficiency and improved generalisation. The framework is trained using a curated glass defect dataset and evaluated using standard performance metrics to demonstrate its robustness and suitability for real-world industrial inspection scenarios. Experimental results show that the optimised ResNet50V2-BO model achieves 98.36% accuracy, along with a precision of 98%, recall of 93%, and F1-score of 95% for the defective class. The confusion matrix and PR curves further confirm the model's ability to reliably distinguish subtle surface defects. These findings highlight the potential of the proposed approach as a fast, reliable, and scalable solution for automated quality control in glass manufacturing industries.
Keywords:
Glass defect classification
Bayesian Optimisation
Gaussian Process (GP)
deep learning
ResNet50V2
Bayesian Optimisation
Gaussian Process (GP)
deep learning
Journal
A
IF:
1.3
Papers:
43
Citations:
902