Detection of crack eggs by image processing and soft-margin support vector machine

Abstract

This paper presented a non-destructive approach for detection of intact and crack eggs using transmission imaging combined with support vector machine classifier. Two hundred brown chicken eggs, including 100 intact and 100 crack eggs were collected as samples. Transmission vision system was developed to capture the sample images. Green color component and edge algorithm based on confidence were then used to transform the color image into edge image for next analysis. Features (mean, variance and third moment) characterizing the differences between crack and intact eggs were extracted through analyzing projection functions as input vectors of the detection model. The detection model in this paper was conducted by support vector machine (SVM). Cracked and Intact eggs could be distinguished by SVM using the statistics parameters. Experimental results showed that the overall identification accuracy in training and test sets were 94% and 93% using 10-fold cross validation approach, respectively.