Defect detection in eggshell using a vision system to ensure the incubation in poultry production

Abstract

This paper describes a method for detecting defects on curved surfaces. In particular, this research focuses on defects in poultry eggs for damage identification in the shell as a result of thin-shelled eggs. The vision system is based on defect detection by scanning a laser pattern of structured light and imaging, highlighting the changes in geometry as a result of deformation of the laser transitions generated by scanning the egg surface. Then, the images are analyzed to obtain equidistant points along the curve and evaluated by creating a cubic spline interpolation. The interpolation allows for the extraction of descriptive metric characteristics to observe the disparity between curves, illustrating the defects by performing graph interposition. The obtained metric information is used to classify the defective samples by developing an algorithm using an artificial neural network, trained with a database composed of 200 images, wich achievies 97.5% efficiency during the evaluation of 150 egg samples. This technique can be applied to detecting corrugated, wrinkled, pimpled, odd- shaped and misshapen eggs.