Штучний інтелект

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ISSN 2710-1673

ONLINE: ISSN 2710-1681

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Дослідження та оптимізація методів виявлення об’єктів на зображеннях

Подоляк Б.1, Філімонова Т.2
1 Державний торговельно-економічний університет
2 Державний торговельно-економічний університет
b.podolyak.fit.122.20@knute.edu.ua; t.filimonova@knute.edu.ua

Повний текст (PDF)

УДК: 004.932
Мова публікації: Англійська
Stuc. intelekt. 2026; 31; (1):49-57

Анотація: This work has conducted research and optimization of object detection methods in images using modern deep learning approaches. The work has conducted a theoretical analysis of the object detection problem, considered the role of computer vision in the modern information environment, and analyzed domestic and foreign scientific and technical sources. An analysis of existing neural network architectures, in particular YOLOv8, Faster R-CNN, and DETR, was conducted, with the determination of their advantages, disadvantages, and areas of effective application. The selected models were optimized by selecting hyperparameters, improving learning processes, and increasing the balance between accuracy and speed. Experimental implementation and comparison of models were conducted, which allowed assessing the impact of the applied optimization methods on the efficiency of detection systems.

Ключові слова: object detection methods, neural networks, computer vision, optimization, YOLOv8, Faster R-CNN, DETR

Посилання:

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  2. S. Ren, K. He, R. Girshick and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 1 June 2017, doi: 10.1109/TPAMI.2016.2577031.
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