  The behavioural research of pigs can be greatly simplified if automatic
recognition systems are used. Especially systems based on computer vision 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 pleasingly good results. Especially object and
keypoint detectors have been used to detect the individual animals. Despite
good results, bounding boxes and sparse keypoints do not trace the contours of
the animals, resulting in a lot of information being lost. Therefore this work
follows the relatively new definition of a panoptic segmentation and aims at
the pixel accurate segmentation of the individual pigs. For this a framework of
a neural network for semantic segmentation, different network heads and
postprocessing methods is presented. With the resulting instance segmentation
masks further information like the size or weight of the animals could be
estimated. The method is tested on a specially created data set with 1000
hand-labeled images and achieves detection rates of around 95% (F1 Score)
despite disturbances such as occlusions and dirty lenses.
