Crack detection in eggs using computer vision and BP neural network

To improve the accuracy of detection and classification of egg with cracks, computer vision and BP neural network technology were synthetically applied to automatically identify and classify the eggs with cracks. First, the images of eggs with or without cracks were captured through computer vision system, then the images were processed, and five geometrical characteristic parameters of crack areas and noise areas were acquired. Second, with the five parameters as inputs, the best BP neural network (5 input nodes, 10 hidden nodes, 2 output nodes) was employed to detect egg crack and classify eggs. The experimental results show that the rate of testing precision of cracked egg reaches 92.9% and the classification accuracy of total eggs can reach 96.8% by the 5-10-2 BP neural network model.