PDF(Pubmed)
Abstract：
We investigate the impact of different data modalities for cattle weight estimation. For this purpose, we collect and present our own cattle dataset representing the data modalities: RGB, depth, combined RGB and depth, segmentation, and combined segmentation and depth information. We explore a recent vision-transformer-based zero-shot model proposed by Meta AI Research for producing the segmentation data modality and for extracting the cattle-only region from the images. For experimental analysis, we consider three baseline deep learning models. The objective is to assess how the integration of diverse data sources influences the accuracy and robustness of the deep learning models considering four different performance metrics: mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2). We explore the synergies and challenges associated with each modality and their combined use in enhancing the precision of cattle weight prediction. Through comprehensive experimentation and evaluation, we aim to provide insights into the effectiveness of different data modalities in improving the performance of established deep learning models, facilitating informed decision-making for precision livestock management systems.
摘要：
我们研究了不同数据模式对牛体重估计的影响。为此,我们收集并呈现我们自己的牛数据集，代表数据模态：RGB，深度,RGB和深度相结合，分割，并结合了分割和深度信息。我们探索了MetaAIResearch提出的基于视觉变换器的零拍模型，用于生成分割数据模态并从图像中提取仅牛区域。为了进行实验分析，我们考虑三个基线深度学习模型。目的是评估不同数据源的集成如何影响深度学习模型的准确性和鲁棒性，考虑四个不同的性能指标：平均绝对误差（MAE）。均方根误差(RMSE)，平均绝对百分比误差(MAPE)，和R的平方(R2)。我们探讨了与每种模式相关的协同作用和挑战，以及它们在提高牛体重预测精度方面的组合使用。通过综合试验和评价,我们的目标是提供对不同数据模式在提高已建立的深度学习模型的性能方面的有效性的见解，促进精准牲畜管理系统的知情决策。
