Real-Time Road Sign Detection with YOLOv5

If you drive - there's a chance you enjoy cruising down the road. A responsible driver pays attention to the road signs, and adjusts their speed in accordance to the laws mandating that you follow the speed limit in a given area, amongst other signs that regulate drivers.

Though - what if you miss a sign? Not everyone has a sidekick to also pay attention and to tell them when there's a change in the speed limit or if there's another sign worth acknowledging. Some cars, especially modern ones, are equiped with cameras that read road signs in real time and show thex current limit on your dashboard. For example, the Citroen C3 has a "Memory" button, which applies the latest noticed speed limit to your cruise control if it's active.

Wouldn't it be nice to have a system that also watches for road signs and gives you audio cues when it sees one?

Whether it's a speed limit sign, a stop sign, or another sign - having a side passenger that reminds you of the signs can be pretty useful, especially if this side passenger doesn't blink, only watches for the signs, and runs on your phone if your car doesn't already have a system built in. My old car doesn't have this system and I'd love to use my already existing phone to also look out for the signs, with no extra cost. Furthermore, if you're app-savvy, you can integrate the model into an application that plays sounds or audio clips of voices calling the road signs out loud.

In this guided project, we'll use a mixture of public datasets, and create our own dataset, manually prepare and label it, train and fine-tune a YOLOv5 model with Transfer Learning to detect road signs. We'll then take a look at how PyTorch models are generally deployed to the web with Flask, as well as Android and iOS devices. This encapsulates the entire life-cycle of an object detection application.

Note: This Guided Project is part of our in-depth course on Practical Deep Learning for Computer Vision, but doesn't require almost any prerequisite knowledge.

Turn Theory Into Practice

All great learning resources, books and courses teach you the holistic basics, or even intermediate concepts, and advise you to practice after that. As soon as you boot up your own project - the environment suddenly isn't as pristine as in the courses and books! Things go wrong, and it's oftentimes hard to pinpoint even why they do go wrong.

StackAbuse Guided Projects are there to bridge the gap between theory and actual work. We'll respect your knowledge and intelligence, and assume you know the theory. Time to put it into practice.

When applicable, Guided Projects come with downloadable, reusable scripts that you can refer back to whenever required in your new day-to-day work.