Ilker Bozcan

Ph.d.-studerende

Ilker Bozcan

Profil

Project title: Vision-based anomaly detection in unmanned aerial vehicle and ground robots

Project description:

Anomaly detection is the classification of objects and events that are labeled as suspicious. Autonomous surveillance systems should be aware of what entities are anomaly in the environment. During this project, we will develop an autonomous surveillance system using cost-effective visual sensors and deep neural networks that are state-of-the-art object detection algorithms.

The system is suited for anomaly detection for both types of data: aerial and ground. Unmanned Aerial/Ground Vehicles (UAV/UGV) are possible robotic platforms to operate anomaly detection systems. As a use case, UGVs will be used for plant classification where weeds are considered as anomaly. In another use case, UAVs will be used for flying object detection where other drones are marked as anomaly.

Supervisor: Assoc. Prof. Erdal Kayacan

ID: 131219197