均方误差
模式
计算机科学
人工智能
平均绝对百分比误差
深度学习
分割
模态(人机交互)
RGB颜色模型
机器学习
平均绝对误差
模式识别(心理学)
人工神经网络
统计
数学
社会科学
社会学
作者
Hina Afridi,Mohib Ullah,Øyvind Nordbø,Solvei Cottis Hoff,Siri Furre,Anne Guro Larsgard,Faouzi Alaya Cheikh
标识
DOI:10.3390/jimaging10030072
摘要
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.
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