Efficient auto-labeling of large-scale poultry datasets (ALPD) using an ensemble model with self- and active-learning approaches

• URL: http://arxiv.org/abs/2501.10809v2
• Date: Sat, 22 Feb 2025 00:11:13 GMT
• Title: Efficient auto-labeling of large-scale poultry datasets (ALPD) using an ensemble model with self- and active-learning approaches
• Authors: Ramesh Bahadur Bist, Lilong Chai, Shawna Weimer, Hannah Atungulua, Chantel Pennicott, Xiao Yang, Sachin Subedi, Chaitanya Pallerla, Yang Tian, Dongyi Wang,
• Abstract summary: This study explores semi-supervised auto-labeling methods, integrating self and active learning approaches to develop an efficient, label-scarce framework for auto-labeling large poultry datasets (ALPD)<n>The results showed that YOLOv8s-World and YOLOv9s performed better when compared performance metrics for broiler and hen detection under supervised learning.<n>The hybrid YOLO-World model, incorporating the optimal YOLOv8s backbone with zero-shot models, demonstrated the highest overall performance.
• Score: 4.6951658997946755
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The rapid growth of artificial intelligence in poultry farming has highlighted the challenge of efficiently labeling large, diverse datasets. Manual annotation is time-consuming and costly, making it impractical for modern systems that continuously generate data. This study addresses this challenge by exploring semi-supervised auto-labeling methods, integrating self and active learning approaches to develop an efficient, label-scarce framework for auto-labeling large poultry datasets (ALPD). For this study, video data were collected from broilers and laying hens housed. Various machine learning models, including zero-shot models and supervised models, were utilized for broilers and hens detection. The results showed that YOLOv8s-World and YOLOv9s performed better when compared performance metrics for broiler and hen detection under supervised learning, while among the semi-supervised model, YOLOv8s-ALPD achieved the highest precision (96.1%) and recall (99%) with an RMSE of 1.87. The hybrid YOLO-World model, incorporating the optimal YOLOv8s backbone with zero-shot models, demonstrated the highest overall performance. It achieved a precision of 99.2%, recall of 99.4%, and an F1 score of 98.7% for detection. In addition, the semi-supervised models with minimal human intervention (active learning) reduced annotation time by over 80% compared to full manual labeling. Moreover, integrating zero-shot models with the best models enhanced broiler and hen detection, achieving comparable results to supervised models while significantly increasing speed. In conclusion, integrating semi-supervised auto-labeling and zero-shot models significantly improves detection accuracy. It reduces manual annotation efforts, offering a promising solution to optimize AI-driven systems in poultry farming, advancing precision livestock management, and promoting more sustainable practices.

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