An Optimized and Accelerated Object Instance Segmentation Model for Low-Power Edge Devices

Deep learning, for sustainable applications or in cases of energy scarcity, requires using available, cost-effective, and energy-efficient accelerators together with efficient models. We explore using the Yolact model, for instance, segmentation, running on a low power consumption device (e.g., Intel Neural Computing Stick 2 (NCS2)), to detect and segment-specific objects. We have changed the Feature Pyramid Network (FPN) and pruning techniques to make the model usable for this application. The final model achieves a noticeable result in Frames Per Second (FPS) on the edge device while achieving a consistent mean Average Precision (mAP).