Development of Attention-Enabled Multi-Scale Pyramid Network-Based Models for Body Part Segmentation of Dairy Cows

Automated assessment of dairy cow traits, important for productivity evaluation, provides advantages by mitigating personal biases, measurement errors, and stress factors typically associated with manual assessment. To develop such a system, the initial step involves accurately segmenting cow body regions for subsequent trait measurement. Thus, the present study introduces a refined DeepLabV3 + CNN model with EfficientNetB2 as the backbone and enhanced with attention mechanisms, aiming for precise segmentation of cow body regions from lateral and posterior views. In the DeepLabV3 + model, various backbone models, including MobileNet, MobileNetV2, MobileNetV3, EfficientNetB0, EfficientNetB1, and EfficientNetB2, were evaluated. Among these, EfficientNetB2 exhibited superior performance in lateral view segmentation, achieving a mean Intersection-over-union (m-IoU) of 94.19