ALGORITHMS FOR AUTOMATIC OBJECT RECOGNITION IN VIDEO DATA

Authors

Keywords:

object detection, video analysis, YOLO, Faster R-CNN, EfficientDet, deep learning, real-time systems, YOLOv8

Abstract

Real-time object detection and recognition in video streams is one of the most important tasks in modern computer vision. This paper compares the performance of modern deep learning models such as YOLOv8, Faster R-CNN, and EfficientDet in object detection in video data. Experiments on the COCO and MOT17 open datasets showed that the YOLOv8 model has the highest speed (145 FPS) and sufficient accuracy (mAP@0.5 = 53.9%), which is superior in real-time applications. At the same time, EfficientDet-D7 (mAP@0.5 = 55.1%) performed best in cases where high accuracy was required.

Author Biographies

Zulunov Ravshanbek Mamatovich, Fergana State Technical University

Candidate of Physical and Mathematical Sciences, Associate Professor of the Fergana State Technical University, Professor

Asadbek Muxtorov, Fergana State Technical University

Software Engineering, Master's degree student, Fergana State Technical University.

References

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Published

2025-12-05

How to Cite

Zulunov, R., & Muxtorov, A. (2025). ALGORITHMS FOR AUTOMATIC OBJECT RECOGNITION IN VIDEO DATA. The Descendants of Al-Fargani, 1(4), 68–70. Retrieved from https://al-fargoniy.uz/index.php/journal/article/view/930

Issue

No. 4 (2025):

Section

Статьи

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Copyright (c) 2025 Зулунов Равшанбек Маматович, Асадбек Мухторов

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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