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I recently received my Master's degree in Electrical and Electronics Engineering Department of Middle East Technical University (METU), where I was co-advised by Prof. Dr. A. Aydin Alatan and Dr. Kutalmis Gokalp Ince, and I am affiliated with the Center for Image Analysis. I had received my two BSc degrees in the EEE and Mathematics departments of METU, with high honors. I'm interested in computer vision, machine learning and image processing. Most of my research is about supervised learning, object detection and tracking. My Master's Thesis work focuses on moving object detection and semi-supervised automated annotation methods. aybora at metu dot edu dot tr CV / Google Scholar / Linkedin / Twitter / Github |
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MSc in Electrical and Electronics Engineering, 2021
BSc in Electrical and Electronics Engineering, 2017
Double Major in Mathematics, 2018
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Please check my Google Scholar page for my complete and up-to-date list of publications. |
 
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Aybora Koksal, Onder Tuzcuoglu, Kutalmis Gokalp Ince, Yoldas Ataseven, A. Aydin Alatan ICIP, 2022 arXiv / video / code In this work, two existing hard example mining approaches (LRM and focal loss, FL) are adapted and combined in a state-of-the-art real-time object detector, YOLOv5. |
 
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Kutalmis Gokalp Ince, Aybora Koksal, Arda Fazla, A. Aydin Alatan ICCV Workshops, 2021 arXiv / code We propose a semi-automatic bounding box annotation method for visual object tracking by utilizing temporal information with a tracking-by-detection approach. |
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Aybora Koksal, Kutalmis Gokalp Ince, A. Aydin Alatan CVPR Workshops, 2020 arXiv / video / code In this work, different types of annotation errors for object detection problem are simulated and the performance of a popular state-of-the-art object detector, YOLOv3, with erroneous annotations during training and testing stages is examined. |
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Shoutout to Jon Barron for the template. |