Introduction: Ceramic tile surface defect detection is crucial for ensuring product quality. This study proposes an integrated approach combining feature engineering and a Defect Fuse Classifier for accurate defect detection. Methods: The proposed model utilizes Python and splits the collected data into 70% for training and 30% for testing. Purpose: The purpose section explicitly states the objectives of the study. It highlights the research goals, such as evaluating the effectiveness of the proposed methodology in detecting ceramic tile surface defects and exploring the impact of parameter variations on detection performance. Results: Comparative analysis with state-of-the-art methods is conducted using various metrics such as sensitivity, specificity, accuracy, precision, FPR, FNR, NPV, F-Measure, and MCC. (a) For a Training Rate of 70%: The proposed Defect Fuse Classifier outperforms existing models with an accuracy of 97.4%, precision of 88.5%, sensitivity of 88.5%, specificity of 98.5%, F-Measure of 88.5%, MCC of 87%, NPV of 98.5%, FPR of 1.4%, and FNR of 11.4%. Conclusion: This study introduces a novel deep learning approach for ceramic tile surface defect detection, encompassing data acquisition, pre-processing, feature extraction, feature selection, and deep learning-based defect detection. The proposed Defect Fuse Classifier, integrating CNN, Bi-LSTM, and RNN, demonstrates superior performance, making it a promising solution for defect detection in ceramic tile surfaces.

Keywords: Ceramic tile, Defect detection, Hybrid optimization model, Defectfuse classifier.