Egg Defect Detection and Classification in Boiled Egg Industry with Surface Disturbance Removal on the Eggshell Based on Image Processing

Abstract

In the boiled egg industry, quality inspection is typically conducted twice: before eggs are transported into the conveyor-based boiling system (before boiling), and after they exit the water-based cooling system prior to packaging (after cooling). These inspections are commonly carried out through human visual assessment, which demands substantial human resources and time. This paper presents an automated system for detecting and classifying defective eggs-such as those with cracks, dents, rough shells, and other surface anomalies-using image processing techniques. The system is designed to enhance the visibility of such defects while minimizing the impact of production-related surface disturbances, including water stains, reflections from the cooling process, and red stamps from imported eggs. The proposed system comprises two main approaches: (1) Defects Detection Method, which classifies eggs into two categories: intact and defective; and (2) Pixel Counting and Comparison Method, which classifies eggs into three categories: intact, cracked or dented, and exploded eggs. This system offers a practical and efficient solution for the egg processing industry, reducing reliance on human labor, minimizing inspection time, and lowering hardware requirements for industrial implementation.