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bioRxiv · 10.1101/2024.12.23.629644

MacqD - A Deep Learning-based Model for Automatic Detection of Socially-housed Laboratory Macaques

Moat, G. J.·Gaudet Trafit, M.·Paul, J.·Bacardit, J.·Ben Hamed, S.·Poirier, C.
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Abstract

Despite advancements in video-based behaviour analysis and detection models for various species, existing methods are suboptimal to detect macaques in complex laboratory environments. To address this gap, we present MacqD, a modified Mask R-CNN model incorporating a SWIN transformer backbone for enhanced attention-based feature extraction. MacqD robustly detects macaques in their home-cage under challenging scenarios, including occlusions, glass reflections, and overexposure to light. To evaluate MacqD and compare its performance against pre-existing macaque detection models, we collected and analysed video frames from 20 caged rhesus macaques at Newcastle University, UK. Our results demonstrate MacqDs superiority, achieving a median F1-score of 99% for frames with a single macaque in the focal cage (surpassing the next-best model by 21%) and 90% for frames with two macaques. Generalisation tests on frames from a different set of macaques from the same animal facility yielded median F1-scores of 95% for frames with a single macaque (surpassing the next-best model by 15%) and 81% for frames with two macaques (surpassing the alternative approach by 39%). Finally, MacqD was applied to videos of paired macaques from another facility and resulted in F1-score of 90%, reflecting its strong generalisation capacity. This study highlights MacqDs effectiveness in accurately detecting macaques across diverse settings.

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Moat, G. J., Gaudet Trafit, M., Paul, J., Bacardit, J., Ben Hamed, S., Poirier, C.. 2024-12-23. MacqD - A Deep Learning-based Model for Automatic Detection of Socially-housed Laboratory Macaques. https://doi.org/10.1101/2024.12.23.629644

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