INTELLIGENT PRINT QUALITY CONTROL USING CNN MODELS - Kaushik, Vaibhav; Thakuriya, Kajal; Selvakumar, Paul Praveen Albert; Khurana, Jatin; Deshmukh, Shrushti; Saritha SR (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Controlling the quality of prints is a vital process in the current printing industries so as to maintain the quality of the mass production in terms of consistency, accuracy, and aesthetic features. The typical machine-vision systems find it difficult to detect the invisible defects like streaks, blotches, misalignment and color differences because of shortcomings of hand-made extraction of features. The proposed research is an intelligent print quality control system based on the Convolutional Neural Network (CNN) models to detect and classify defects automatically. The method starts with the generation of a set of complete dataset which consists of high-resolution images of controlled light and camera conditions. All images are annotated and labelled based on certain types of defects in order to enable supervised learning. The preprocessing of the data, such as normalization, resizing, augmentation, color enhancement, etc. are used to enhance model robustness and generalization. Various CNNs, including VGG, ResNet and MobileNet, are studied using the method of transfer learning to maximize the accuracy and performance. The CNN model created to be customized comprises of other layers as well covering localization of defect and region based detection. The real-time image capture, CNN inference and decision-making modules are incorporated into the system architecture, and executed at the edge computing devices or on the GPU, to offer them fast processing. The classification accuracy and real-time processing results suggest that the experimental results are better than the traditional vision-based systems.