Abstract

Introduction: Object detection has been an essential task in computer vision for decades, and modern developments in computer vision and deep learning have greatly increased the accuracy of detecting systems. However, the high computational requirements of deep learningbased object detection algorithms limit their applicability to resource-constrained systems, such as embedded devices.

Methods: With the advent of Tiny Machine Learning (TinyML) devices, such as Raspberry Pi, it has become possible to deploy object detection systems on small, low-power devices. Due to their accessibility and cost, Tiny-ML devices, such as Raspberry Pi, a single-board tiny-ML device that is extremely well-liked, have recently attracted a lot of attention.

Results: In this study, we present an enhanced SSD-based object detection approach and deploy the model using a tinyML device, i.e., Raspberry Pi.

Conclusion: The proposed object detection model is lightweight and built utilizing Mobilenet- V2 as the underlying foundation.

Keywords: SSD, object detection, CNN, DL, Tiny-ML, MoblienetV2, Raspberry-pi.

Graphical Abstract