Animal excretions in form of urine puddles and feces are a significant source of emissions in livestock farming. Automated detection of soiled floor in barns can contribute to improved management processes but also the derived information can be used to model emission dynamics. Previous research approaches to determine the puddle area require manual detection of the puddle in the barn. While humans can detect animal excretions on thermal images of a livestock barn, automated approaches using thresholds fail due to other objects of the same temperature, such as the animals themselves. In addition, various parameters such as the type of housing, animal species, age, sex, weather and unknown factors can influence the type and shape of excretions. Due to this heterogeneity, a method for automated detection of excretions must therefore be not only be accurate but also robust to varying conditions. These requirements can be met by using contemporary deep learning models from the field of artificial intelligence. This work is the first to investigate the suitability of different deep learning models for the detection of excretions in pigsties, thereby comparing established convolutional architectures with recent transformer-based approaches. The detection models Faster R-CNN, YOLOv8, DETR and DAB-DETR are compared and statistically assessed on two created training datasets representing two pig houses. We apply a method derived from nested cross-validation and report on the results in terms of eight common detection metrics. Our work demonstrates that all investigated deep learning models are generally suitable for reliably detecting excretions with an average precision of over 90%. The models also show robustness on out of distribution data that possesses differences from the conditions in the training data, however, with expected slight decreases in the overall detection performance.

翻译：动物排泄物（如尿液水坑和粪便）是畜牧业排放的重要来源。自动化检测畜舍地面污染不仅能改进管理流程，其衍生信息还可用于排放动态建模。以往测定水坑面积的研究方法需在畜舍内人工识别排泄区域。虽然人类能够通过畜禽舍热成像图像识别动物排泄物，但基于阈值的自动化方法常因其他同温物体（如动物本体）的干扰而失效。此外，围栏类型、动物种类、年龄、性别、天气及未知因素等多种参数均可能影响排泄物的形态特征。鉴于这种异质性，自动化排泄物检测方法不仅需要精确性，还必须具备应对多变条件的鲁棒性。通过采用人工智能领域的当代深度学习模型可满足这些要求。本研究首次探讨了不同深度学习模型在猪舍排泄物检测中的适用性，对比了成熟的卷积架构与新兴的基于Transformer的方法。我们在两个代表性猪舍构建的训练数据集上，对Faster R-CNN、YOLOv8、DETR和DAB-DETR四种检测模型进行了比较与统计评估。采用基于嵌套交叉验证的衍生方法，依据八项常用检测指标报告实验结果。研究表明，所有考察的深度学习模型均能可靠检测排泄物，平均精度超过90%。这些模型对训练数据分布外的样本也表现出鲁棒性，尽管检测性能存在预期范围内的轻微下降。