  Precision livestock farming (PLF) increasingly relies on advanced object
localization techniques to monitor livestock health and optimize resource
management. This study investigates the generalization capabilities of YOLOv8
and YOLOv9 models for cow detection in indoor free-stall barn settings,
focusing on varying training data characteristics such as view angles and
lighting, and model complexities. Leveraging the newly released public dataset,
COws LOcalization (COLO) dataset, we explore three key hypotheses: (1) Model
generalization is equally influenced by changes in lighting conditions and
camera angles; (2) Higher model complexity guarantees better generalization
performance; (3) Fine-tuning with custom initial weights trained on relevant
tasks always brings advantages to detection tasks. Our findings reveal
considerable challenges in detecting cows in images taken from side views and
underscore the importance of including diverse camera angles in building a
detection model. Furthermore, our results emphasize that higher model
complexity does not necessarily lead to better performance. The optimal model
configuration heavily depends on the specific task and dataset. Lastly, while
fine-tuning with custom initial weights trained on relevant tasks offers
advantages to detection tasks, simpler models do not benefit similarly from
this approach. It is more efficient to train a simple model with pre-trained
weights without relying on prior relevant information, which can require
intensive labor efforts. Future work should focus on adaptive methods and
advanced data augmentation to improve generalization and robustness. This study
provides practical guidelines for PLF researchers on deploying computer vision
models from existing studies, highlights generalization issues, and contributes
the COLO dataset containing 1254 images and 11818 cow instances for further
research.
