An optimized deep learning approach to detect and classify defective tiles in production line for efficient industrial quality control

In this paper, a deep learning-based machine vision approach is proposed to automatically detect and classify defective tiles in the production assembly line of a tile manufacturing industry. The deep learning model used in this methodology is trained with 30,000 real-time images of cement/ceramic tiles, and the features of the image samples are extracted using the convolutional layers in the model. The defective tiles are identified and classified using an optimized activation function that acts as the decision-making layer or output layer of the deep learning model. The performance of this deep learning technique is evaluated using various metrics like accuracy, precision, recall and f1-score which is further compared with state-of-the-art activation functions like Relu, sigmoid, tanh and softmax. To further enhance the performance metrics, the feature extraction is done using various pre-trained models like VGG-16, Resnet50 and InceptionV3 and was further evaluated using metrics like K (Kappa statistic), OA (overall accuracy) and AA (average accuracy). The obtained experimental results with an accuracy of 99.96% under a favorable learning rate prove the robustness and efficiency of the proposed methodology to enhance industrial quality control in any tile manufacturing industry.