SAM for Poultry Science

TL;DR

This study evaluates the zero-shot segmentation and tracking capabilities of the Segment Anything Model (SAM) in poultry science, demonstrating its superior performance over existing models and its potential for analyzing chicken behavior.

Contribution

The paper explores SAM's application in poultry industry, specifically in chicken segmentation and tracking, highlighting its effectiveness and laying groundwork for future research.

Findings

SAM outperforms SegFormer and SETR in chicken segmentation

SAM enables effective chicken tracking and behavior analysis

The study demonstrates SAM's potential in poultry science applications

Abstract

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.