Amazon is splashing out on new vehicle inspectors to watch for damage or wear to its vast fleet of delivery vans--and they're not human. The retailer is installing camera-studded inspection stations equipped with artificial intelligence-powered technology called AVI, or automated vehicle inspection, at hundreds of its distribution centers worldwide. When a driver working out of any of the 20 delivery centers currently equipped with the tech returns their vehicle at the end of a shift, they slowly drive it through a sensor-laden archway made by startup UVeye, which has headquarters in the US and Israel. The technology is made up of three separate high-res camera systems: One scans a vehicle's undercarriage, another checks tire quality, and another focuses on the vehicle exterior. The data they gather is compiled into a 3D image of the vehicle and used by machine-learning software to identify whether the vehicle is damaged or needs maintenance.

This is a fascinating discussion on how UVEye is applying computer vision and machine learning to vehicle inspection for use cases such as terrorism prevention. Sramana Mitra: Let's start by introducing our audience to yourself as well as to UVEye. Amir Hever: I'm the Co-Founder and CEO of UVEye. We are introducing to the market the new standard of inspection. There are a lot of different use cases to understanding the exterior or expanse of a vehicle that range from security to commercial. That includes car manufacturers, public transportation, rental companies, and insurance companies. Sramana Mitra: Let's take three customer use cases that illustrates three different aspects of how you create value. You choose whatever you want to highlight. Amir Hever: The first use case would be the one that we started with. It was a security organization trying to understand whether the vehicle was modified, especially in the undercarriage. If someone attaches something to the

Early on in the process, pi-top's Mechanical Design Engineer, Chris started making plans for our robot but everything changed in January after a brief discussion with our CTO Ryan, which led to Robo-Top becoming something of a pi-top Frankenstein's Monster. As I'm not massively technically-minded, Chris is going to take over the blog post from here and fill you all in with the journey from Brian's Max Robot to pi-top's monster. After deciding to mount a pi-top on the robot, the first we had to do was a space claim analysis. We had to compare the size of a pi-top to the robot space claim specified in the PiWars' rules. Our pi-top is 340mm wide and 220mm deep.

Amir Hever was driving into a government facility a few years ago when he discovered a huge flaw in their security process. As he approached the entrance gate, a security guard dropped to his knees to look underneath his vehicle. "When he stood up, I asked him what he was looking for," said Hever, CEO and co-founder of computer vision startup UVeye. "The security guard answered honestly that he was looking for threats but actually couldn't see anything. That's when I realized that something wasn't working right."