Efficient Method Using Attention Based Convolutional Neural Networks for Ceramic Tiles Defect Classification

Abstract

One of the main ways of economic development has been to improve the performance and efficiency of manufacturing systems. Indeed, competition is fierce between most industrial organizations to provide new products and richer services for customers who are increasingly demanding. Although, indeed, most of the quality control and visual inspection tasks are performed by humans who use only the naked eye to detect defects, this form of control presents a number of limits, such as the size of the defects impossible to detect, risk of marketing a defective product and reduce the production performance. In this paper, a new method based on deep learning in the context of ceramic tile defect detection on a conveyor is proposed in order to provide effective quality control and real-time inspection. Our model is based on a convolutional architecture with a convolutional block attention module (CBAM) to pay more attention to the relevant areas of the input image and overcome the spatial information loss problems. A pre-processing step is performed before training by processing each image corresponding to a type of defect with an appropriate mask to facilitate learning. The experimental results show that our model produces an accurate and efficient classification of ceramic tile defects with a reduced number of parameters. We also propose a novel Ceramic defect tile dataset obtained from a ceramic production unit. The results of the experiments show that the suggested approach reaches an average accuracy rate of 99.93% compared to the state-of-the-art.