Learning to make drones ‘see’ with artificial intelligence

When I initially got the itch to purchase a ‘beginner’ level drone a few months back, I didn’t really know why I wanted it. I didn’t want to pay big bucks because odds were I’d lose it, crash it or simply not appreciate it so I bought a sub $100 DJI ‘Tello’ drone (<a href="https://www.jbhifi.com.au/products/ryze-tello-drone-powered-by-dji-white?ab_version=A&gclid=CjwKCAjw04yjBhApEiwAJcvNocwxeBcF2aBRJ7eK6HNRzHXZd1JD0tFlR8Enl1BAEfd0PINashDaMRoCLoQQAvD_BwE" rel="noopener ugc nofollow" target="_blank">this </a>one if you’re interested). I figured it’d be amusing for a while. A neat diversion to buzz around the backyard on a sunny day. It was a tiny little thing, with its four wee propellers and demure front-facing camera. But when I learned that this unassuming flying machine could be interfaced to using Python and the <code>djitellopy</code> library, I suddenly became way more interested. A drone I could program? That has a camera? Oh yes, this is much more my speed. I felt an AI/ML experiment coming on and decided there and then that I was going to set myself the weekend goal to see if I could leverage machine learning to get this drone to perform near real-time object recognition. In other words, teach the drone to see. <h1>The Setup</h1> It might seem counterintuitive to show you the end result at the beginning, but I think giving you an architecture overview first provides useful end-to-end context. From there we can walk through each component, and the what/how/why of it coming into the picture. If you stick around until the end you’ll hopefully see a drone that can perform in-flight object recognition as it whizzes around the place. <a href="https://betterprogramming.pub/learning-to-make-drones-see-with-artificial-intelligence-8a2ac0570db">Visit Now</a>