Solution to MATLAB and Simulink Challenge project 248: Cone Detection for Formula Student Driverless Competition

Probem Statement»

About The Project

The ability to detect cones in the scene is crucial for autonomous driving applications, such as autonomous racing, as it enables the vehicle to navigate through a course safely and efficiently. In the Formula Student driverless competitions, the teams are required to navigate through a series of cones, and detecting the cones accurately can give the team a competitive edge. In this project, we will learn how to use MATLAB® and Simulink® to detect cones in a virtual environment, which can help them gain valuable experience in autonomous driving.

Training Procedure

• VideoLabeler in Matlab is used to label the Blue and Yellow cones as training data.
  
• The labeled data is exported into Matlab workspace.
  
• In the TrainingScript, YOLOv2 object detection algorithm is trained for detection of the cones with labeled training data.
  (Alternatively, already trained network can also be loaded into workspace with yolov2ConeDetector.)
  
• VideoTestingScript runs trained network on Test_Vid.mp4 for detection of the Blue and Yellow cones and labels them.
  

Testing Evaluation

1. Place all the files in the working directory.
2. Load trained network into the Workspace with YOLOv2ConeDetector.
3. Open VideoTestingScript.
4. Run the script.

Test_Vid.mp4

Object.Detection.mp4

Background Material

1. How to Perform Data Labeling for Camera and Lidar Sensor Data
2. YOLOv2 Object Detection: Data Labelling to Neural Networks in MATLAB