Research on Real-time Identification and Tracking Method of Defective Eggs Based on Deep Learning

At present, although there is a high recognition rate in the research on the identification of defective eggs on the factory production line, there are few studies on the application and tracking of defective eggs in the actual production line. This paper takes the eggs on the actual production line as the research object, and proposes a YOLOv4-based defect egg identification and detection method and a Kalman filter-based tracking method. In this study, a database was established for each egg to record its trajectory, category, defect occurrence time, time to start and stop tracking, etc. The egg position was estimated using the algorithm based on Kalman filter, and accurate identification results were obtained. The egg movement state is predicted, and the egg database is updated after matched the prediction result with the recognition result, so as to realize the tracking of the egg and solve the problem that the defect area of the egg is occluded. The experimental results show that the average recognition accuracy of broken eggs of the YOLOv4 model is 94.62%. The tracking accuracy of broken eggs among defective eggs is 100%, and the detection speed is more than 30 frames per second. The task of tracking broken eggs has been preliminarily realized, and the problem that the damaged area of broken eggs is occluded has been solved, laying a foundation for the subsequent elimination of defective eggs.