Colaco Savina Jassica, Kim Jung Hwan, Poulose Alwin, Neethirajan Suresh, Han Dong Seog
School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.
School of Data Science, Indian Institute of Science Education and Research (IISER), Thiruvananthapuram 695551, India.
Animals (Basel). 2023 Mar 28;13(7):1184. doi: 10.3390/ani13071184.
Thermal imaging is increasingly used in poultry, swine, and dairy animal husbandry to detect disease and distress. In intensive pig production systems, early detection of health and welfare issues is crucial for timely intervention. Using thermal imaging for pig treatment classification can improve animal welfare and promote sustainable pig production. In this paper, we present a depthwise separable inception subnetwork (DISubNet), a lightweight model for classifying four pig treatments. Based on the modified model architecture, we propose two DISubNet versions: DISubNetV1 and DISubNetV2. Our proposed models are compared to other deep learning models commonly employed for image classification. The thermal dataset captured by a forward-looking infrared (FLIR) camera is used to train these models. The experimental results demonstrate that the proposed models for thermal images of various pig treatments outperform other models. In addition, both proposed models achieve approximately 99.96-99.98% classification accuracy with fewer parameters.
热成像在禽类、猪和奶牛养殖中越来越多地用于检测疾病和应激。在集约化养猪生产系统中，早期发现健康和福利问题对于及时干预至关重要。使用热成像进行猪的治疗分类可以改善动物福利并促进可持续养猪生产。在本文中，我们提出了一种深度可分离 inception 子网络（DISubNet），这是一种用于对四种猪治疗进行分类的轻量级模型。基于修改后的模型架构，我们提出了两个 DISubNet 版本：DISubNetV1 和 DISubNetV2。我们提出的模型与其他常用于图像分类的深度学习模型进行了比较。由前视红外（FLIR）相机捕获的热数据集用于训练这些模型。实验结果表明，所提出的用于各种猪治疗热图像的模型优于其他模型。此外，两个提出的模型在参数较少的情况下实现了约 99.96 - 99.98% 的分类准确率。
