In recent years, the agricultural industry has witnessed significant
advancements in artificial intelligence (AI), particularly with the development
of large-scale foundational models. Among these foundation models, the Segment
Anything Model (SAM), introduced by Meta AI Research, stands out as a
groundbreaking solution for object segmentation tasks. While SAM has shown
success in various agricultural applications, its potential in the poultry
industry, specifically in the context of cage-free hens, remains relatively
unexplored. This study aims to assess the zero-shot segmentation performance of
SAM on representative chicken segmentation tasks, including part-based
segmentation and the use of infrared thermal images, and to explore
chicken-tracking tasks by using SAM as a segmentation tool. The results
demonstrate SAM's superior performance compared to SegFormer and SETR in both
whole and part-based chicken segmentation. SAM-based object tracking also
provides valuable data on the behavior and movement patterns of broiler birds.
The findings of this study contribute to a better understanding of SAM's
potential in poultry science and lay the foundation for future advancements in
chicken segmentation and tracking.

此研究旨在评估 SAM 在代表性的鸡分割任务（包括基于部件的分割和红外热成像的使用）中的零样本分割效果，并探索使用 SAM 作为分割工具的鸡追踪任务。研究结果表明，与 SegFormer 和 SETR 相比，在整体和基于部件的鸡分割方面，SAM 具有卓越的性能。基于 SAM 的对象跟踪还为肉禽鸟的行为和移动模式提供了有价值的数据。