Triton UAS

Triton UAS

Triton UAS is an engineering student organization at UC San Diego that develops deep learning systems for use on unmanned aerial vehicles.

Robotics, Artificial Intelligence

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Description

Triton UAS develops winged unmanned aerial vehicles with imaging systems to compete in the international AUVSI-SUAS competition against universities around the world. Our goals are to develop a system capable of autonomous flight, navigation, and remote sensing in order to perform a series of specific tasks set by the competition. The main goal of the competition is to detect and recognize ground targets in real time. We have used CNNs in the past for certain parts of our system along with more traditional machine learning techniques and are currently working on converting most of the system to utilize deep learning techniques.

Marco F. added photos to project Triton UAS

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Triton UAS

Triton UAS develops winged unmanned aerial vehicles with imaging systems to compete in the international AUVSI-SUAS competition against universities around the world. Our goals are to develop a system capable of autonomous flight, navigation, and remote sensing in order to perform a series of specific tasks set by the competition. The main goal of the competition is to detect and recognize ground targets in real time. We have used CNNs in the past for certain parts of our system along with more traditional machine learning techniques and are currently working on converting most of the system to utilize deep learning techniques.

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Marco F. created project Triton UAS

Medium 8d3f9093 8bc6 40ff 8255 f5421e9631d6

Triton UAS

Triton UAS develops winged unmanned aerial vehicles with imaging systems to compete in the international AUVSI-SUAS competition against universities around the world. Our goals are to develop a system capable of autonomous flight, navigation, and remote sensing in order to perform a series of specific tasks set by the competition. The main goal of the competition is to detect and recognize ground targets in real time. We have used CNNs in the past for certain parts of our system along with more traditional machine learning techniques and are currently working on converting most of the system to utilize deep learning techniques.

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