Afridi Hina, Ullah Mohib, Nordbø Øyvind, Hoff Solvei Cottis, Furre Siri, Larsgard Anne Guro, Cheikh Faouzi Alaya
Department of Computer Science, Norwegian University of Science and Technology, 2815 Gjøvik, Norway.
Geno SA, Storhamargata 44, 2317 Hamar, Norway.
J Imaging. 2024 Mar 21;10(3):72. doi: 10.3390/jimaging10030072.
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 -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.
我们研究了不同数据模态对牛体重估计的影响。为此，我们收集并展示了我们自己的牛数据集，该数据集代表了以下数据模态：RGB、深度、RGB与深度组合、分割以及分割与深度信息组合。我们探索了Meta AI Research提出的一种基于视觉Transformer的最新零样本模型，用于生成分割数据模态并从图像中提取仅包含牛的区域。为了进行实验分析，我们考虑了三个基线深度学习模型。目的是评估不同数据源的整合如何影响深度学习模型的准确性和鲁棒性，同时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）和决定系数（R2）。我们探索了与每种模态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联合使用。通过全面的实验和评估，我们旨在深入了解不同数据模态在改善既定深度学习模型性能方面的有效性，为精准畜牧管理系统提供明智决策提供便利。
