In this blog we will be seeing how YOLOv5 can be used for people detection and counting. Our dataset is a variety of clips from an India vs. Pakistan cricket match, covering all your favourite shots and moments! Yes, including all the random clips of camera wala dada focusing on pretty women in the crowd (No YOLO model can beat their detection skills in this matter for sure).
Before we get into that, let’s take a quick look at the Flo Edge One, a must-have in every AI and robotics engineer’s toolbox. Here are some remarkable benchmarks that make it a top competitor in edge devices!
- Pre-installed with Ubuntu 22.04 and tools like ROS2, OpenCV, TFlite, etc.
- Qualcomm Adreno 630 GPU.
- 12 MP 4k camera at up to 30fps
- Inferencing yolov5 at 47 milliseconds producing a smooth output of around 20 fps.
And many such awesome features. Check out the Flo Edge One Wiki for more details and even some example models. Keep reading!
Introduction:
YOLO, or “You Only Look Once,” is an amazing algorithm loved by AI engineers because it’s all about detecting things in real-time. The latest version, YOLOv5, is even better because it’s the first of its kind built on PyTorch. That means it is part of PyTorch’s large ecosystem, making it accessible to a vast research community. It is super fast and accurate, plus, its weight files are almost 90 percent smaller than those of its predecessors which means it can run on embedded with ease!
Now are we running this model? This question brings us to something exciting for AI enthusiasts like yourself! SBCs are a must in your toolbox I’m sure. You can build so many different applications that can run independently on that tiny device. But shortage and supply issues these days make them so scarce and expensive. But guess what? We’ve got something even cooler called Flo Edge One! It’s an SBC with a built-in IMU and a 12MP camera. How awesome is that? It comes pre-installed with Ubuntu 22.04, ROS2, OpenCV, and various other tools and packages, making it the perfect choice for your robotics products. It’s powered by a Snapdragon 845 chip and has tons of cutting-edge features. And the best part? It’s affordable too! So you can explore and create without breaking the bank.
Dataset and Model:
The YOLOv5 model is training on the COCO database which contains over 330,000 images and 90 different labels like people, cars, trucks, fruits, animals, and other commonly seen objects. This trained model is used to detect people real time in the match footage. The model has an inference time of 40 to 50 milliseconds per frame, which means it applied the model and, detects and classifies any person in the frame within a fraction of seconds, thereby giving a smooth, uninterrupted output of 20 fps.
Usage:
If you’re looking for a powerful and efficient way to run the Midas model, look no further than the Flo Edge One. This impressive device boasts a light GPU that can deliver smooth results at around 20 FPS, all while maintaining a high level of accuracy. The Flo Edge One is truly a remarkable device, providing a plethora of impressive features that make it a must-have for tech enthusiasts. With its onboard camera, inbuilt IMU, and GNSS capabilities, this device truly has it all. It comes pre-installed with Ubuntu 22.04, ROS2, OpenCV, and various other tools, making it the perfect choice for your robotics ventures. Take for example a brand new marketing campaign that you deployed for your new product. Wanna know how impactful it’s been? Using the 12MP camera on the Flo Edge you can monitor your store inflow and count the number of people walking in since the campaign using this YOLO model. The best part? Amidst the semiconductor crisis, the Flo Edge One is affordable and ready to ship! So you can get started ASAP, without breaking the bank.
Also checkout the Flo Edge One Wiki and our GitHub examples to run this model easily.
Performance Analysis:
The model is capable of detecting people with a 91% confidence. As a person progresses into the frame, the model score goes from around 50% with no prominent features other than the arm, leg or the shoulder, to 90% once the entire person is in the frame. The counter is updated per frame as well, showing the accurate number of people at any moment, even if all the people are not entirely visible. This makes it suitable for complex applications where people need to be detected accurately in real-time with minimal latency.
Conclusion:
The YOLOv5 object detection model is a powerful tool that can detect objects with a very high accuracy. Coupled with Flo Edge One’s low power GPU and 12 MP camera, a wide range of object detection applications like surveillance, security, and sports analysis, can be run with ease.
