Ceramic Defect Detection with Computer Vision

Ceramic tiles are vulnerable to various defects, such as chipping, cracks, and holes, making quality assurance crucial before distribution. This guide illustrates how computer vision, a modern machine learning variant, can be employed to detect these defects using a pre-trained model. Unlike traditional purpose-built cameras, computer vision allows for customizable logic to perform multiple checks simultaneously, enhancing defect detection capabilities using any camera streamed to a computer. The guide recommends using the Roboflow Universe, a platform with a vast community of open computer vision models and datasets, to streamline the process. It provides a step-by-step approach to creating a Roboflow account, testing the model, and deploying it, either in the cloud or on local hardware for maximum performance. Emphasizing the advantages of training a fine-tuned model with data from specific manufacturing environments, the guide suggests integrating active learning to improve model accuracy and prevent drift over time. Additionally, it offers insights into building business logic using Roboflow Templates for practical applications, such as automated rejection systems and trend analysis, to enhance ceramic tile manufacturing processes.