This paper compares contour-based image processing techniques with the YOLOv5 model's deep learning approach for automated ceramic tile inspection. It also introduces a prototype for grading tiles based on their defects. In industries where manual inspections are still common, maintaining quality and efficiency in tile production is crucial. Manual inspection practices are often inefficient, error-prone, and time-consuming. As a result, tile production companies face challenges such as raw material wastage and lost profits from defective products. To address these issues, this research proposes an automatic tile inspection system and provides a comparative analysis of the accuracy rates of each model, including their respective strengths and weaknesses. The goal of this research is to enhance quality control by detecting defects, which in turn will reduce costs and human errors. The effectiveness of the proposed system will be measured by determining whether each tile is rejected as faulty.