Mark Hamazaspyan is a Machine Learning specialist with 3+ years of experience in computer vision, 4+ years of experience in traditional data science, and 2+ experience in ML engineering and MlOps. He has completed various projects, including computer vision, time series, and event data. Mark Hamazaspyan has a teaching experience in universities and private organizations.
2021-present - Applied Statistics and Data Science, Yerevan State University.
2017-2021 - BA in Business: Economics, American University of Armenia
Traditional methods of detecting and mapping utility poles are manual, time-consuming and costly processes. Current solutions focus on detection of T-shaped (cross-arm shaped poles) and the lack of labeled data makes it difficult to generalize the process of other types of poles. This work aims to use Few Shot Object Detection techniques to overcome the unavailability of the data and to create a general pole detection model with few labeled images.