Advancing ICON with Object Avoidance
Anshul B -
Hello and welcome back to my blog! My name is Anshul Baddi, and I’m excited to share the latest updates on my research project, ICON. Over the past week, I have been working with Professor Ryan Woodward and his fellow students at GCU to learn more about the technology that autonomous cars use. I have been learning about CNN’s from his classes and online courses. Our first phase is object avoidance.
Object Avoidance: The First Phase of ICON
ICON’s initial goal is to classify and identify objects and then decide whether to steer away or stop. This phase ensures the car can react to obstacles using real-time image classification.
Required Hardware
To achieve this, we will be integrating:
- Raspberry Pi 5 with 8GB RAM: Ensures smooth processing at around 15-30 FPS.
- Camera: Captures images for classification, and we might use the Rasberry Pi AI camera, which has the MobileNet ML model built into it.
- Dual Motor Driver Carrier: Enables the Raspberry Pi to send commands to motors.
Learning Phase: Understanding CNNs
This past week, I have been focusing on Convolutional Neural Networks (CNNs) to understand their role in object detection. A CNN is a type of neural network that consists of layers to classify images.
Key CNN Components
- Convolutional Layer: Extracts image features.
- ReLU Activation: Introduces non-linearity to the images to exemplify features.
- Pooling Layer (Max Pooling): Reduces dimensions while retaining important features and also removes around 75% of the data
- Fully Connected Layer: Maps features to classification labels.
What’s Next?
Now that I have a foundational understanding of CNNs, the next steps involve implementing a CNN-based object detection model into the Rasberry Pi. We will also begin hardware integration once our components arrive. Stay tuned for more updates!
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