Ala'a Alshubbak

"Develop a Deep Learning-Based Multi-model for object Detection in Agricultural and suburban Environment"

( MPI-SWS talk in Kooperation mit dem Fachbereich Informatik)

Abstract der Arbeit: Object detection is one of the most important tasks in computer vision. It has been improved recently by the invention of fast computing and GPU to reach deep learning techniques. Most of the recent research on such a field is focusing on the detection of multiple objects in urban systems. Such as detection of pedestrians for safety purposes, or detection of traffic signs for autonomAbstract der Arbeit: ous vehicles. From which, different types of datasets have been captured and used to develop new approaches and algorithms to enhance the detection process. Unfortunately, little attention has been made to detecting objects in suburban environments or working areas

This research is focusing on developing a new approach/es to solve and improve the detection of humans and other important objects in such a challenging environment. Such approaches mainly are developed from the existing deep learning techniques , that used convolutional neural networks either in anchor-based models or anchor-free models.

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