Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
National Innovation Center of Digital Technology in Animal Husbandry, Beijing 100097, China.
Sensors (Basel). 2024 Oct 2;24(19):6385. doi: 10.3390/s24196385.
Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioral assessments, enabling a comprehensive evaluation of the hens' health, behavior, and population counts. To address the issue of insufficient data samples in the health detection process for individual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environments, various data enhancement methods such as rotation, shearing, color enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for applying visible light images and corresponding thermal infrared images in the health detection, behavioral analysis, and counting of caged laying hens under large-scale farming.
考虑到动物福利，散养蛋鸡养殖模式越来越受到关注。然而，在一些国家，大规模养殖仍然依赖于笼养模式，因此关注笼养蛋鸡的福利同样重要。为了评估笼养蛋鸡的健康状况，建立了一个包含可见光和热红外图像的数据集，进行形态学、热成像、鸡冠和行为评估等分析，从而可以对母鸡的健康、行为和群体计数进行全面评估。为了解决个体和群体母鸡健康检测过程中数据样本不足的问题，构建了一个名为 BClayinghens 的数据集，其中包含 61,133 张可见光和热红外图像。该数据集 BClayinghens 使用三种设备完成：智能手机、可见光摄像机和红外热像仪。所有的热红外图像都对应可见光图像，并通过坐标校正实现了位置对齐。此外，可见光图像被标注了鸡头标签，获得了 63,693 个鸡头标签，可直接用于训练鸡头目标检测的深度学习模型，并结合相应的热红外数据来分析鸡头的温度。为了使构建的深度学习目标检测和识别模型能够适应不同的养殖环境，对图像进行了各种数据增强处理，如旋转、剪切、颜色增强和噪声添加。BClayinghens 数据集对于应用可见光图像和相应的热红外图像在大规模养殖环境下进行笼养蛋鸡的健康检测、行为分析和计数非常重要。
