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（以尿渍和粪便形式）是畜牧业中重要的排放源。自动检测畜舍地板污染状况有助于优化管理流程，且获取的信息可用于模拟排放动态。既往确定排泄物区域的研究方法需人工定位猪舍内的排泄物。尽管人类可通过牲畜棚舍热成像图像识别动物排泄物，但基于阈值的自动化方法会因同温物体（如动物本身）的存在而失效。此外，圈舍类型、动物物种、年龄、性别、天气及未知因素等变量均会影响排泄物的形态与性状。因此，自动化排泄物检测方法需兼具精准性与针对多变条件的鲁棒性。当代人工智能领域的深度学习模型可满足上述要求。本研究首次探究不同深度学习模型在猪舍排泄物检测中的适用性，对比了经典卷积架构与近期基于Transformer的方法。我们在代表两栋猪舍的两个训练数据集上，对Faster R-CNN、YOLOv8、DETR和DAB-DETR检测模型进行统计评估与比较。通过嵌套交叉验证方法，我们报告了八项常见检测指标的结果。研究表明，所有被测试的深度学习模型均普遍适用于可靠检测排泄物，平均精度超过90%。模型对训练数据条件存在差异的分布外数据亦展现出鲁棒性，但整体检测性能略有可预期的下降。