Measurement of Shank Length in Live Chickens Using Visible-Infrared Image Fusion and Keypoint Prediction

Accurate shank-length phenotyping of live chickens is hindered by feather occlusion and uncertain endpoint localization in visible images. We developed a two-stage method that fuses registered visible and infrared images before predicting the two measurement endpoints. The fusion network used a U-Net encoder-decoder with residual blocks, Coordinate Attention, and Strip Pooling, trained with a YUV-guided loss. A YOLOv8s-Pose model with Coordinate Attention and a length-related loss then localized the endpoints. The dataset comprised 100 chickens and 1000 paired visible-infrared acquisitions, separated at the individual level into training, validation, and test sets. On the test set, at the chicken level, the reported mean signed difference, mean absolute error, and root mean square error were 0.129 mm, 0.790 mm, and 0.984 mm, respectively. The Pearson correlation coefficient between model-derived and manual reference measurements was 0.992. Compared with either single-modality input, the fused images reduced the mean absolute error and root mean square error. These findings show that complementary texture and thermal-boundary information can support accurate vision-based shank-length measurement under controlled acquisition conditions.