Modeling And Detection of Cracks in Earthenware Water Jugs Using Artificial Neural Networks and Image Processing Techniques

Quality control in traditional pottery production is a labor-intensive process that relies heavily on manual visual inspection to detect structural defects. To address this, the present study proposes an automated framework combining image processing techniques with Artificial Neural Networks (ANN) to detect and classify cracks in handmade earthenware jugs. Images acquired via smartphone were preprocessed using Otsu’s thresholding and median filtering to effectively isolate defect regions and suppress noise. A dataset of 189 samples was utilized to train and test the ANN model. The proposed model achieved a classification accuracy of 92.98%. Confusion matrix analysis confirmed robust performance, demonstrating high capability in distinguishing between intact and defective samples with no significant bias toward either class. This approach offers potential for modernizing pottery production, enhancing efficiency, and ensuring quality.