OpenCV Raspberry Pi Self Driving Car using Neural Networks - Part1/3

Summary

In this tutorial, the creator walks through the process of building a self-driving car using neural networks. The video focuses on data collection, where data is gathered via a joystick, motor, webcam, and organized into folders with corresponding steering angles. The collected data is then saved in log files and images. Future steps include training the model with this data and integrating features like traffic and object detection. The creator also discusses testing the system on an Nvidia Jetson Nano to assess speed and accuracy. Viewers are encouraged to join the Discord channel for project discussions and troubleshooting.

Takeaways

• 😀 Data collection is the first step in creating a self-driving car using neural networks. This involves collecting images and corresponding steering angles.
• 😀 The data collection process involves four key modules: joystick, data collection, motor, and webcam, each responsible for a specific task.
• 😀 A unique timestamp is used to name image files to prevent overwriting when new data is collected, ensuring each image has a distinct filename.
• 😀 The collected data, consisting of images and their steering angles, is saved in separate folders, with each run generating a new folder (e.g., images_0, images_1).
• 😀 The data collection module records images from the webcam and stores them with the corresponding steering angle, which is controlled via the joystick.
• 😀 The log file (CSV) contains the paths to all images and their corresponding steering angles, essential for training the neural network later.
• 😀 The system allows for easy data collection without restarting the script, as it uses joystick buttons to start and stop the recording of images and angles.
• 😀 After data collection, the logs are saved using pandas, making it easy to organize and manage the collected data for training.
• 😀 The collected data is stored in images folders and log files, allowing for multiple data collection sessions without losing previous data.
• 😀 The next phase of the project involves using the collected data to train a neural network, with future additions such as traffic and object detection planned.
• 😀 The system is designed to be modular, with different files for each function (e.g., joystick, motor, webcam), which can be easily updated and integrated with other systems like the Nvidia Jetson Nano.

Q & A

What are the primary modules used in the self-driving car project?

The primary modules used are the joystick module for control input, the motor module for car movement, and the webcam module for capturing images.

How is the data for the neural network collected?

Data is collected by capturing images through the webcam while the car is controlled using the joystick. The corresponding steering angles are saved in a log file, and the images are saved with unique timestamps to avoid overwriting.

What happens when the data collection script is run?

When the script runs, it collects images at regular intervals (e.g., every 10 frames) and saves them with timestamps. The steering angle corresponding to each image is logged in a separate file for later use in model training.

How are images stored during data collection?

Images are stored in folders named sequentially (e.g., `images0`, `images1`, etc.) to organize the captured data. Each image file is named with a timestamp to ensure unique filenames and avoid overwriting.

What is the purpose of the log file in the data collection process?

The log file records the steering angles corresponding to the captured images. It serves as a reference for training the model, enabling it to learn the relationship between the visual input (images) and the motor's steering commands.

How can the data collection process be stopped?

The data collection can be stopped manually by pressing a button on the joystick, after which the collected data is saved and can be used for training the neural network.

What is the next step after collecting the data?

The next step is training the neural network using the images and log files collected. This will allow the car to predict steering angles based on new input data.

What are the future plans for the self-driving car project?

Future plans include adding features like traffic sign detection and object detection. Additionally, the system will be tested on the NVIDIA Jetson Nano to compare its performance in terms of speed and accuracy.

How can users seek help or share their projects?

Users can join the Discord channel to share their projects, ask questions, or troubleshoot issues. The community can assist with resolving code errors or providing feedback on projects.

Why is the project being tested on the NVIDIA Jetson Nano?

The project will be tested on the NVIDIA Jetson Nano to compare the system's performance, specifically focusing on differences in speed and accuracy when running on this hardware versus other platforms.