The challenge of crack detection

The detection of cracks in eggs is a critical aspect of quality control in the egg industry. Cracked eggs can lead to contamination and reduce the shelf life of the product, posing health risks to consumers. Traditionally, acoustic-based inspection has been the primary method for identifying cracked eggs. However, this process is sensitive and requires supervision and maintenance of the sensing mechanisms.

Enter AI-based computer vision

AI-based Computer Vision systems have emerged as a game-changer in the detection of cracks in eggs. These systems utilize advanced algorithms and deep learning techniques to analyze high-resolution images of eggs, identifying cracks with remarkable accuracy and efficiency.

How AI-based computer vision works?

The process begins with the acquisition of high-quality and multiple images of individual eggs or groups of eggs using specialized cameras. These images are then fed into a computer vision system equipped with AI algorithms. The AI algorithms analyze the images, extracting relevant features and patterns associated with cracks. The system learns from a vast dataset of annotated images, allowing it to differentiate between normal eggs and those with cracks.

The AI model employs convolutional neural networks (CNNs) to recognize intricate crack patterns, leveraging its ability to detect subtle variations in color, texture, and shape. Through iterative training and validation, the model continuously improves its accuracy and adaptability, becoming highly proficient in distinguishing between different types of cracks, such as hairline cracks or larger fractures.

Benefits of AI-based crack detection

1. Enhanced Accuracy: AI-based computer vision systems can detect cracks with higher precision compared to acoustic based detection systems, minimizing the risk of false negatives or false positives. This leads to improved quality control and reduces the likelihood of defective eggs reaching the market.
2. Non-touch inspection: The non-touch inspection process ensures no cross-contamination from egg to egg and as the inspection system has no moving parts, the maintenance is reduced to an absolute minimum.
3. Generally: Functionality in the AI-based Computer Vision system can gradually be extended with the already known computer vision based dirt and leak detection systems. Even AI-based egg weighing systems can be implemented. However, in regards to weighing all investigations show until further that the accuracy is far from known conventional methods available in the market, and can lead to undesired and unacceptable weighing results.
4. This means that all quality inspections of eggs gradually will be implemented in one AI-based Computer Vision system and thereby the egg handling during and after the inspection will be simplified creating opportunities to have an even more compact and robust egg sort-out system. 

Future outlook and conclusion

As AI and Computer Vision technologies continue to advance, the potential applications within the egg industry are vast. Beyond crack detection, AI-based systems could be used for analyzing other quality parameters such as size uniformity, or abnormalities in egg shapes.

Moreover, the integration of AI-powered computer vision with robotics and automation could lead to fully autonomous egg sorting and packing systems, further streamlining the production process while maintaining stringent quality standards.

AI-based computer vision is transforming the egg industry by providing an accurate, efficient, and cost-effective solution for crack detection. By automating this crucial quality control process, producers can ensure consumer safety, improve efficiency, and reduce costs. With ongoing advancements, AI-based systems are poised to revolutionize the egg industry, setting new standards for quality assurance in food production.

Jan Holm Holst
R&D Director, Vice President