An Industrial Application Towards Classification and Optimization of Multi-Class Tile Surface Defects Based on Geometric and Wavelet Features

Abstract

It is possible to detect visual surface defects with software in industrial tile production and increase productivity by automating the quality control process. In this process, low error rate and low cost are important indicators. In order to eliminate this negativity and the effect of the human factor, error detection software has been developed in an artificial intelligence-based industrial artificial vision environment. Spots, scratches, cracks, pore defects, which are the most common surface defects, are classified according to 6 different geometric and wavelet transform attributes. Firstly, an industrial artificial vision environment was created. In this environment, a total of 150 tile images, equal numbers from each class, were obtained on the real-time production line. The resulting images were converted into binary images by preprocessing and filtering. For classification, the support vector machines method, which performs high in two-class classifications, is used with the one versus all approach. In classifications made using RBF kernel function using wavelet features as classification performance, a higher success was achieved in all defect classes than geometric features. Real-time application software for all these processes has been developed with the Python language on Ubuntu operating system.

Human Sitting Postures Classification Based on Angular Features with Fuzzy-Logic Labeling

“…The cv was performed without data sharing between training and validation data to avoid overtraining. In order to measure the performances of each model, a multi-class confusion matrix which is defined in [25] and the ROC curve, is created, and Accuracy (A), Recall (R), precision (P), F1-score (F), AUC (Area Under Curve), LogLoss (LLlogistic loss) and Specificity (S) indicators are calculated to evaluate performance [25].…”