In this paper we focus on the role of deep instance segmentation of laboratory rodents in thermal images. Thermal imaging is very suitable to observe the behaviour of laboratory animals, especially in low light conditions. It is an non-intrusive method allowing to monitor the activity of animals and potentially observe some physiological changes expressed in dynamic thermal patterns. The analysis of the recorded sequence of thermal images requires smart algorithms for automatic processing of millions of thermal frames. Instance image segmentation allows to extract each animal from a frame and track its activity and thermal patterns. In this work, we adopted two instance segmentation algorithms, i.e., Mask R-CNN and TensorMask. Both methods in different configurations were applied to a set of thermal sequences, and both achieved high results. The best results were obtained for the TensorMask model, initially pre-trained on visible light images and finally trained on thermal images of rodents. The achieved mean average precision was above 90 percent, which proves that model pre-training on visible images can improve results of thermal image segmentation.
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