Defective egg detection based on deep features and Bidirectional Long-Short-Term-Memory

Eggs are one of the most important nutritional sources worldwide. In addition, defects that may occur in the eggshells endanger food safety and cause adverse effects on production companies. Automatic separation of defective eggs from quality eggs is an important issue due to economic and health reasons. On this motivation, a real-time machine vision system based on deep learning has been developed for the detection of cracked, bloody and dirty eggs. In this study, a continuous rotating system was designed to visualize all surfaces of the egg. Thus, the adverse conditions such as dirt, blood, and cracks that may occur on any surface of the egg have been successfully monitored. In the proposed system for the detection of robust eggs, deep features are extracted using a pre-trained residual network model and then the obtained features are fed into the Bidirectional Long-ShortTerm-Memory (BiLSTM). The efficiency of the proposed model was calculated using dirty, bloody, cracked and robust egg images with the developed machine vision system. The experimental works showed that the proposed model achieved a 99.17% accuracy score. The obtained result was also compared with state-of-the-art methods, and the proposed model was observed to exhibit the highest accuracy among the compared methods.