TinyML-On-The-Fly: Real-Time Low-Power and Low-Cost MCU-Embedded On-Device Computer Vision for Aerial Image Classification
Riya Samanta, Bidyut Saha, Soumya K. Ghosh
Abstract
Aerial image classification is essential to intelligent surveillance and monitoring systems. Traditional computer vision methods either uses computational offloading to high-end servers or edge devices. However, unmanned aerial vehicles (UAVs) platforms have resource and power constraints. Aerial image classification is complicated and less-expensive UAVs lack processing power and cameras. Even with large-scale computing environments, methods for classifying images are difficult to apply to aerial imagery. We propose TinyAerialNet leveraging TinyML for real-time inference on a resource-constrained ESP32 CAM. The model tested on AIDER dataset, achieves 88% on-device accuracy in the micro-controller with 103.9 KB RAM and 850 milliseconds for inference.