AI Egg Inspection for Automated Eggshell Defect Detection and Classification

Case Overview

Solution: SolVision

High-speed egg production lines require consistent detection of eggshell cracks and pores before distribution. SolVision uses deep learning-based visual inspection to identify surface defects and classify eggs according to defined quality levels.

The Case

Automated Eggshell Inspection and Quality Grading

Eggshell integrity is essential for maintaining product quality and reducing contamination risks. Cracks can compromise the shell’s protective barrier, while surface porosity is another characteristic used to assess eggshell quality.

Eggs must be inspected and graded before distribution to identify defective products and maintain consistent quality standards. Automated visual inspection reduces reliance on manual defect identification and classification.

The Challenge

Detecting Irregular Defects on Moving Eggshells

Eggshell pores and cracks vary in size, shape, and distribution, making consistent defect detection difficult. Random surface patterns further complicate eggshell quality classification.

High-speed production lines and varying egg orientations introduce additional challenges. Traditional inspection systems struggle to accommodate these variations, while manual inspection is inefficient for continuous production.

The Solution

Deep Learning-Based Eggshell Defect Detection with SolVision

SolVision uses deep learning-based visual inspection to detect eggshell pores and cracks. Annotated sample images train the AI model to recognize surface defects despite variations in their size, shape, and distribution.

The trained model analyzes eggshell images to identify defects and classify eggs according to defined quality levels, automating tasks previously performed through manual inspection.

Egg Quality Classification

SolVision classifies eggs into three quality levels based on detected eggshell characteristics. Color-coded annotations distinguish each classification: