EGG QUALITY DETECTION BASED ON LIGHTWEIHT HCES-YOLO

The quality detection of eggs based on deep learning faced many problems, such as similar feature colors and low computational efficiency, which resulted in an increased probability of false detection or missed detection. To effectively solve these problems, this paper proposed an egg quality detection method based on YOLOv8n, which integrated the ContextGuideFusionModule, EfficientHead, and SIOU loss functions by improving the backbone network. The recognition rate from the field test was 88.4%, indicating that the algorithm could meet the real-time monitoring requirements, effectively identify the quality status of eggs, and provide support for intelligent poultry house management.