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NEWS AI

Innovative Uses of YOLO Vision Model in Real-World Edge Computing Applications

DFRobot Jun 12 2024 1002

In today's rapidly advancing technological landscape, edge computing and computer vision are transforming various industries at unprecedented speed and depth. The YOLO (You Only Look Once) model, as one of the most advanced real-time object detection systems, is at the forefront of this revolution. With its exceptional speed and accuracy, the application of YOLO in edge computing has brought revolutionary changes to multiple sectors. This article explores the practical use of the YOLO model in edge computing, highlighting innovations and impacts in fields such as autonomous driving, smart surveillance, manufacturing, and precision agriculture. Additionally, we will delve into maker projects involving the deployment of YOLO models on single-board computers (SBC) using OpenVINO, showcasing how hobbyists and developers can leverage this technology to create innovative solutions and drive industry progress.

 

Introduction to YOLO Vision Model

The YOLO (You Only Look Once) model is an advanced object detection system renowned for its speed and accuracy. Unlike traditional methods, YOLO frames object detection as a single regression problem, directly predicting bounding box coordinates and class probabilities from image pixels. Its end-to-end approach enables YOLO to process images in real-time, making it an ideal choice for applications requiring instant feedback and decision-making.

 

Key Advantages of YOLO in Edge Computing

  • 1. Real-time Processing Capability: The YOLO model can process images at a speed of 45 frames per second, which is crucial for applications requiring quick decision-making, such as autonomous driving and intelligent surveillance.
  • 2. High Precision Detection: Through innovative architecture design, the YOLO model achieves high-precision object detection, capable of identifying multiple objects within a single image.
  • 3. Unified Architecture: The YOLO model employs a single network to predict both bounding boxes and class probabilities, simplifying the computation process and enhancing detection speed and efficiency.

 

Industry Application Cases of the YOLO Model

1. Autonomous Driving

In the field of autonomous driving, the YOLO model, combined with edge computing, enables real-time object detection and decision-making. Vehicles need to quickly recognize and respond to pedestrians, vehicles, and obstacles on the road, and YOLO's high-speed processing capability ensures the safety and reliability of this process. For instance, many new energy vehicle manufacturers are utilizing the YOLO model to enhance their vehicles' environmental perception, thereby improving the safety and efficiency of autonomous driving.

Case Description: A new energy vehicle is equipped with an autonomous driving system powered by the YOLO model. During its drive, the system detects a pedestrian suddenly appearing in front of the vehicle in real-time, prompting the vehicle to quickly react, decelerate, and stop, avoiding a potential collision. This reaction time is only a few milliseconds, showcasing the excellent performance of the YOLO model in high-speed environments.

The autonomous driving system detects a person in front of the car and displays it in real-time
Figure: The autonomous driving system detects a person in front of the car and displays it in real-time

 

The autonomous driving system successfully detects traffic lights
Figure: The autonomous driving system successfully detects traffic lights (Source: Tesla Driver / YouTube)

 

2. Intelligent Surveillance

In intelligent surveillance systems, the combination of the YOLO model and edge computing significantly enhances the safety of public spaces. Through real-time video analysis, these systems can detect and respond to suspicious activities, identify unauthorized entries, and issue instant alerts, thereby improving security efficiency and effectiveness.

Case Description: On a construction site, the YOLO model is used to detect whether workers are wearing personal protective equipment (such as helmets), improving site safety and management efficiency. The system can efficiently identify workers not wearing protective gear in complex environments and promptly issue alerts, reducing the occurrence of safety incidents and enhancing site safety management levels.

The YOLO safety detection system on a construction site
Figure: The YOLO safety detection system on a construction site

 

3. Manufacturing and Quality Control

In the manufacturing industry, the YOLO model combined with edge computing technology significantly enhances quality control and operational efficiency on production lines. Real-time product detection systems can quickly identify and eliminate defects, ensuring the production of high-quality products.

Case Description: In the industry, some automotive manufacturers have applied a vision detection system based on the YOLO model on their production lines to ensure the quality of each component. This system can detect minute defects in parts in real-time, such as cracks, dimensional deviations, and assembly errors, significantly reducing the risk of defective products entering the market while improving production efficiency and quality assurance.

The YOLO quality detection system on an automotive production line
Figure: The YOLO quality detection system on an automotive production line

 

4. Precision Agriculture

In precision agriculture, the combination of the YOLO model and edge computing technology improves crop yield and quality through real-time monitoring and analysis of crop health.

Case Description: Some agri-tech companies have adopted drones and ground sensors equipped with the YOLO model to monitor the health of farmland crops. These systems can detect pests and diseases in real-time, assess soil conditions, and help farmers optimize production practices. On a farm, a drone equipped with the YOLO model conducts routine flights and detects early signs of pest infestation in a cornfield, enabling timely intervention measures that significantly increase crop yield and quality while reducing pesticide usage.

Drone detection system equipped with the YOLO model

Drone detection system equipped with the YOLO model
Figure: Drone detection system equipped with the YOLO model

 

Maker Project: Deploying the YOLO Model on Single Board Computers

In the maker community, combining edge computing with the YOLO model opens up numerous possibilities for innovation and DIY projects. Many tech enthusiasts and developers use single board computers (SBCs) such as LattePanda, Raspberry Pi, and NVIDIA Jetson to deploy YOLO models on these small yet powerful devices for various interesting and practical projects.

 

OpenVINO Project on LattePanda

LattePanda 3 Delta is a high-performance SBC featuring an 11th Gen Intel quad-core processor, suitable for various AI and ML projects.

  • Project Description: By running the OpenVINO toolkit on LattePanda 3 Delta, developers can efficiently deploy YOLO models for real-time object detection. This project demonstrates how to optimize the YOLOv8 model using OpenVINO for efficient edge computing. Steps include downloading the dataset, validating the model, converting it to OpenVINO IR format, quantizing the model, and using it in real-time video streams.
  • Detailed Tutorial: OpenVINO running YOLOv8 model on LattePanda 3 Delta for object detection.

Lattepanda 3 Delta high-performance SBC
Figure: Lattepanda 3 Delta

 

YOLOv5 Deployment on Raspberry Pi

Raspberry Pi is a well-known SBC in the maker community, widely used in various DIY projects.

  • Project Description: YOLOv8 can efficiently run on Raspberry Pi for object detection and image recognition. By using Roboflow and Docker on Raspberry Pi, developers can deploy YOLOv8 models for real-time monitoring, home security, and other applications. The project details how to install and configure YOLOv8 on Raspberry Pi and perform object detection.
  • Detailed Tutorial: Running YOLOv8 on Raspberry Pi.

Deploy YOLO model on Raspberry Pi 4
Figure: Deploy YOLO model on Raspberry Pi 4

 

YOLO Deployment on NVIDIA Jetson

The NVIDIA Jetson series is a high-performance SBC designed for AI and ML applications, capable of efficiently running YOLO models for projects like robotics, drones, and smart surveillance.

  • Project Description: Deploying YOLO models on Jetson Nano enables efficient object detection and real-time video analysis. This project demonstrates how to install YOLO on Jetson Nano, optimize it using TensorRT, and improve the model's inference speed and efficiency. Steps include installing PyTorch and Torchvision, configuring DeepStream, generating YOLOv5 configuration and weight files, and performing real-time inference.
  • Detailed Tutorial: Running YOLO on NVIDIA Jetson Nano.

Deploy YOLO model on Jetson Nano
Figure: Deploy YOLO model on Jetson Nano

 

Conclusion

Combining the YOLO model with edge computing is driving innovation and development across various industries, offering superior performance, real-time processing capabilities, and enhanced data security. From autonomous driving to intelligent surveillance, manufacturing to precision agriculture, and maker projects, YOLO's applications are extensive and transformative. This article showcases practical cases of YOLO model applications in different fields, highlighting its powerful capabilities as a real-time object detection system. We can anticipate more innovative applications in the future, further advancing industry development and progress. The combination of edge computing and the YOLO model not only brings efficiency and safety improvements today but also lays a solid foundation for future intelligent development.


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