Insights on back marking for the automated identification of animals
This work addresses the need for guidelines on back mark design to support automated monitoring in animal research and real-world applications, though it is incremental as it builds on existing machine learning methods.
The study tackled the problem of designing back marks for uniform-looking species like pigs to enable effective individual-level monitoring via machine learning, finding that marks must remain unambiguous under motion blur, diverse view angles, occlusions, and common data augmentations.
To date, there is little research on how to design back marks to best support individual-level monitoring of uniform looking species like pigs. With the recent surge of machine learning-based monitoring solutions, there is a particular need for guidelines on the design of marks that can be effectively recognised by such algorithms. This study provides valuable insights on effective back mark design, based on the analysis of a machine learning model, trained to distinguish pigs via their back marks. Specifically, a neural network of type ResNet-50 was trained to classify ten pigs with unique back marks. The analysis of the model's predictions highlights the significance of certain design choices, even in controlled settings. Most importantly, the set of back marks must be designed such that each mark remains unambiguous under conditions of motion blur, diverse view angles and occlusions, caused by animal behaviour. Further, the back mark design must consider data augmentation strategies commonly employed during model training, like colour, flip and crop augmentations. The generated insights can support individual-level monitoring in future studies and real-world applications by optimizing back mark design.