Identification of the Big and Small Ends of Eggs by Developing a Novel Model with a Combination of RCSOSA and CSFCN Modules for Robot Packaging of Poultry Eggs

Accurate identification of the big and small ends of poultry eggs is a crucial step in automatic packaging of poultry eggs in trays. In the actual packaging process, some factors, such as uneven surface brightness, different eggshell colors, and differences in ends and shape, have a negative impact on recognition accuracy. In this study, three kinds of eggs with red-shell, white-shell, and green-shell were used as the research object. After image acquisition, the contrast stretching method was utilized to correct the brightness of the collected egg images, and the U-Net network was utilized to achieve egg body region segmentation and complex background removal. The A2C2f_RCSOSA, EUCB, and CSFCN modules were introduced for improvement in YOLOv12. The results showed that the RCCS12 model had the best overall performance. Its precision, recall, and mAP@0.5 reached 99.5%, 99.4%, and 99.5%, respectively. Compared to RepVitASF12 (i.e., a superior model for egg recognition in our previous research), RCCS12 maintains high precision while having lower FLOPs, model size, and inference time. The overall results indicated that the integration of reasonable brightness correction and recognition detection algorithms can effectively solve the problem of big and small ends recognition, providing a reference for the automatic egg packaging process.