- Lovpreet Hansra, Computer Science BS
- Adam Porter, Electrical Engineering BS
- Luan Nguyen, Aerospace Engineering BS
Our goal was to provide our car with particular GPS coordinates, have the car navigate to the destination coordinates by using the magnetometer within the IMU, and then search for a particular object within a given radius. We plan to utilize computer vision to distinguish between objects and find the desired one while the car circumnavigates the area.
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For object recognition, we implemented a YOLO algorithm trained using the COCO dataset which classifies 80 different classes of objects.
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The yolo algorithm essentially using bounding boxes to locate and classify objects in a given image. To classify the objects, I used a CNN using the darknet framework but implemented using pytorch. The model uses leaky ReLU as its activation function, and has a convolutional layer and pooling layer at every step. I started with open source code and expanded it to work with the video from our jetson.