Measurement Method for the Egg Shape Index of Breeding Egg Based on a Lightweight YOLOv12n-Seg Model

To address the strong reliance on manual operations and the low efficiency of egg shape index (ESI) phenotyping in layer breeding, this study proposed an ESI measurement method based on an improved YOLOv12n-seg model. Ghost Bottleneck modules were introduced into the backbone to reduce model complexity. In addition, a boundary-aware loss combining Binary cross entropy (BCE), Dice, and Boundary Loss was designed to improve mask quality. Based on the segmentation results generated by YOLO-Ghost, principal component analysis was employed to extract the orientation and scale of the principal axes of the segmented regions. The major and minor axes of the pixel-level masks were then obtained, and their ratio was used as the measured ESI value. Compared with YOLOv12n-seg, YOLO-Ghost reduced the number of model parameters and computational cost by 39.86% and 17.58%, respectively, while increasing the frame rate by 40.91%. The model achieved an mAP@0.50–0.95 of 92.10%, BF1 of 86.28%, and BIoU of 74.99%. Compared with other instance segmentation models, YOLO-Ghost achieved a precision of 99.96%, a recall of 99.69%, and a detection speed of 454.55 f/s. For ESI estimation, the predicted values showed good agreement with manual measurements, with an R2 of 0.8184, MAE of 0.03219, and RMSE of 0.03681. The results indicate that the proposed method can achieve non-contact, automated, and accurate measurement of ESI, and provides technical support for high-throughput automated phenotypic data collection in layer breeding.