Detection of Broken Eggs with Deep CNNs and Performance Analysis of Models

Food safety and quality are extremely important for consumer satisfaction. Eggs are an important source of protein worldwide and occupy an important place in many industries. Therefore, detecting and preventing damage that may occur in the egg production process is of great importance in terms of both protecting the health of the consumer and reducing the costs in the egg industry. However, the suitability and quality of eggs for consumption depends on various external factors throughout their journey. Among these factors, physical damage is one of the most common and important. In the egg industry, damage to eggs and therefore not being able to reach the consumer is an important cost factor. This means a great loss, especially for businesses that produce on an industrial scale. Damage detection and prevention methods can help reduce these losses. In this study, a dataset containing a total of 569 images in three classes was used to detect egg damage. There are three classes in the dataset named crack, empty and good. For the classification of images, VGG16 and VGG19 models from CNN (Convolutional Neural Network) architectures were used with transfer learning method. As a result of the classification, 93.8% classification success was obtained from the VGG16 model and 95.1% from the VGG19 model. F1 Score, precision and recall metrics of the classification performance of the model were also analyzed. By considering the advantages and disadvantages of detecting egg damage in the image, a basis for future studies in this field has been established.