Behavioural research of pigs can be greatly simplified if automatic recognition systems are used. Systems based on computer vision in particular have the advantage that they allow an evaluation without affecting the normal behaviour of the animals. In recent years, methods based on deep learning have been introduced and have shown excellent results. Object and keypoint detector have frequently been used to detect individual animals. Despite promising results, bounding boxes and sparse keypoints do not trace the contours of the animals, resulting in a lot of information being lost. Therefore, this paper follows the relatively new approach of panoptic segmentation and aims at the pixel accurate segmentation of individual pigs. A framework consisting of a neural network for semantic segmentation as well as different network heads and postprocessing methods will be discussed. The method was tested on a data set of 1000 hand-labeled images created specifically for this experiment and achieves detection rates of around 95% (F1 score) despite disturbances such as occlusions and dirty lenses.

中文翻译：

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用于姿势识别的个体猪的全景分割。

如果使用自动识别系统，猪的行为研究可以大大简化。基于计算机视觉的系统尤其具有以下优点：它们可以在不影响动物正常行为的情况下进行评估。近年来，基于深度学习的方法被引入并取得了良好的效果。物体和关键点检测器经常用于检测个体动物。尽管结果很有希望，但边界框和稀疏关键点无法追踪动物的轮廓，导致大量信息丢失。因此，本文遵循相对较新的全景分割方法，旨在对个体猪进行像素精确分割。将讨论由用于语义分割的神经网络以及不同的网络头和后处理方法组成的框架。该方法在专门为此实验创建的 1000 张手工标记图像的数据集上进行了测试，尽管存在遮挡和脏镜片等干扰，但检测率仍达到 95% 左右（F1 分数）。