MODERN METHODS OF OBJECT DETECTION IN COMPUTER VISION

Vladislav Potapov; Valentyn Liubchenco

doi:10.36074/logos-13.03.2026.027

MODERN METHODS OF OBJECT DETECTION IN COMPUTER VISION

Vladislav Potapov; Valentyn Liubchenco

Conference proceedings: Collection of scientific papers «ΛΌГOΣ» with Proceedings of the VII International Scientific and Practical Conference «Theoretical and practical aspects of modern scientific research» (March 13, 2026; Seoul, South Korea)

Section: Information technologies and systems

Publication date: 2026/03/13

Pages: 151-154

DOI: 10.36074/logos-13.03.2026.027

ISBN: 978-617-8440-88-6

Publisher: Case Co., Ltd.

Language: en

PDF for indexing Original PDF in OJS archive DOI

Abstract

Object detection is a fundamental task in computer vision, which involves determining the presence of objects of specific classes in an image or video frame and localizing their spatial position. The result of the detection algorithm is usually a set of bounding boxes and their corresponding object classes.

Author affiliations

Vladislav Potapov: Kharkiv National University of Radio Electronics, UKRAINE
Valentyn Liubchenco: Kharkiv National University of Radio Electronics, UKRAINE; ORCID

References

REFERENCES:
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016). SSD: Single Shot MultiBox Detector. Proceedings of the European Conference on Computer Vision (ECCV), 21–37. https://arxiv.org/abs/1512.02325
Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems (NeurIPS), 28, 91–99. https://arxiv.org/abs/1506.01497
Xu, C., Wang, Y., Zhang, H., & Sun, J. (2022). PP-YOLOE: An evolved version of YOLO. arXiv preprint. https://arxiv.org/abs/2203.16250
Zhao, Y., Lv, Y., Xu, S., Wei, S., & Liu, J. (2023). RT-DETR: Real-Time Detection Transformer. arXiv preprint. https://arxiv.org/abs/2304.08069

[1] REFERENCES:

[2] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016). SSD: Single Shot MultiBox Detector. Proceedings of the European Conference on Computer Vision (ECCV), 21–37. https://arxiv.org/abs/1512.02325

[3] Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems (NeurIPS), 28, 91–99. https://arxiv.org/abs/1506.01497

[4] Xu, C., Wang, Y., Zhang, H., & Sun, J. (2022). PP-YOLOE: An evolved version of YOLO. arXiv preprint. https://arxiv.org/abs/2203.16250

[5] Zhao, Y., Lv, Y., Xu, S., Wei, S., & Liu, J. (2023). RT-DETR: Real-Time Detection Transformer. arXiv preprint. https://arxiv.org/abs/2304.08069