Scale Your Fleet Of TinyML Solutions

Scale your parking lot monitoring system or any TinyML solution using AWS IoT, Edge Impulse and Balena.

Introduction

This project is a proof of concept to demonstrate how easily you can deploy a fleet of edge devices running tinyML model with object detection capability. I have chosen a simple use case of counting cars in a parking lot. You can use similar infrastructure to build any kind of objection detection use cases such as identifying product defect in real manufacturing workflow, automate car inspection for exterior damages etc.Parking facilities need to know how many cars are parked in a given facility at any given point of time, to evaluate how many empty parkings available and intake more customers. You also want to keep track of the number of cars that enter and exit your facility during any given time. You can use this information to improve operations, such as adding more parking payment centers, optimizing price, directing cars to different floors etc. Parking center owners typically operate on multiple floor or more than one facility and they want to manage a fleet of devices and aggregate real-time data to take efficient decision on business process.

Software

• Sign up for a free Edge Impulse account
• Sign up for a free BalenaCloud account
• balenaEtcher
• Sign up for a free AWS account
• Python 3.7+ and pip

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