Detecting Mice Depth Shapes with Deployable Tiny Neural Networks in Small Cages

Detecting tiny shapes, per pixel, of mammals can be challenging in small environments, such as the cages used for pre-clinical studies. There are tens of racks containing hundreds of cages in a typical installation. In such a context, this paper examined several neural models, taking into account their computational and memory complexity, to ensure that they can be deployed on tiny off-the-shelf and cheap microcontrollers. The study employed a NUCLEO board with STM32U5 connected to a time of flight sensor named VL53L8CX. This sensor generated sequences of depth frames over time. These data were collected into a hand-crafted dataset, which, following a data augmentation process, achieved a totals of 2498 samples. The models considered were: a U-Net based architecture; a hand-crafted one, called Micronet. The latter, following the post-training quantization procedure to 8 bits integer, achieved a mAP score above 86%, an inference time of 0.27 ms, a FLASH size of 1.3 KiB and a RAM usage of 0.5 KiB. These results enabled the implementation of a high-speed detector on cheap microcontroller chip.