Evaluation of deep learning techniques in road sign recognition

Latafat A. Gardashova, Haji Hajiyev Haji Hajiyev

Кимёвий технология. Назорат ва бошқарув · 2025-yil

Annotatsiya

Deep learning has transformed the computer vision field and greatly improved the performance and efficiency of road sign recognition systems. This research compares different deep learning methods, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models, in terms of their ability to effectively detect and classify road signs under various conditions. The study compares performance measures such as accuracy, processing speed, and robustness to environmental conditions like low lighting, occlusion, and adverse weather. The results show that CNN-based methods, especially those with transfer learning and ensemble techniques, have better performance in real-time scenarios. Problems like computational complexity and data quality issues are also discussed, as well as some possible solutions for deep learning model optimization for road sign detection. The paper emphasizes the necessity of the integration of such techniques into autonomous driving systems and intelligent transportation systems for the enhancement of road safety and traffic control. Furthermore, the research highlights the growing importance of scalable and adaptable architectures that can be efficiently deployed on embedded devices with limited computational resources. Ethical considerations such as algorithmic transparency, potential bias in datasets, and regulatory compliance are also touched upon. Future work may explore the fusion of multimodal data sources (e.g., LiDAR, GPS) to improve detection reliability and support context-aware decision-making in autonomous vehicles.

Maqola ma’lumotlari
MualliflarLatafat A. Gardashova, Haji Hajiyev Haji Hajiyev
JurnalКимёвий технология. Назорат ва бошқарув
NashriyotТошкент давлат техника университети, «Ўзқурилишматериаллар» ДАК, «Ўзкимёсаноат» ДАК, «Совпластитал» ҚҚ, Ўзбекистон Интеллектуал мулк агентлиги
ISSN2181-1105, 1815-4840
Nashr sanasi2025-09-03
Jild2025
Son4
Betlar81-89
TilIngliz
Ilmiy sohaTexnika
DOI10.59048/2181-1105.1701
Indeksga qo‘shilgan2026-09-02

DOI: 10.59048/2181-1105.1701 · Maqolaning asl sahifasi

Ilmiy soha

Texnika

Iqtibos keltirish

APALatafat A. Gardashova, & Haji Hajiyev Haji Hajiyev (2025). EVALUATION OF DEEP LEARNING TECHNIQUES IN ROAD SIGN RECOGNITION. Кимёвий технология. Назорат ва бошқарув, 2025(4), 81-89. https://doi.org/10.59048/2181-1105.1701
GOST (OAK)Latafat A. Gardashova, Haji Hajiyev Haji Hajiyev EVALUATION OF DEEP LEARNING TECHNIQUES IN ROAD SIGN RECOGNITION // Кимёвий технология. Назорат ва бошқарув. – 2025. – T. 2025, № 4. – P. 81-89. – DOI: 10.59048/2181-1105.1701.
MLALatafat A. Gardashova, and Haji Hajiyev Haji Hajiyev. “EVALUATION OF DEEP LEARNING TECHNIQUES IN ROAD SIGN RECOGNITION.” Кимёвий технология. Назорат ва бошқарув, 2025(4), 81-89, 2025. https://doi.org/10.59048/2181-1105.1701
ChicagoLatafat A. Gardashova, and Haji Hajiyev Haji Hajiyev. “EVALUATION OF DEEP LEARNING TECHNIQUES IN ROAD SIGN RECOGNITION.” Кимёвий технология. Назорат ва бошқарув, 2025(4), 81-89 (2025). https://doi.org/10.59048/2181-1105.1701
HarvardLatafat A. Gardashova, and Haji Hajiyev Haji Hajiyev (2025) ‘EVALUATION OF DEEP LEARNING TECHNIQUES IN ROAD SIGN RECOGNITION’, Кимёвий технология. Назорат ва бошқарув, 2025(4), 81-89. https://doi.org/10.59048/2181-1105.1701
BibTeX
@article{latafatagardashova2025,
  title = {EVALUATION OF DEEP LEARNING TECHNIQUES IN ROAD SIGN RECOGNITION},
  author = {Latafat A. Gardashova and Haji Hajiyev Haji Hajiyev},
  journal = {Кимёвий технология. Назорат ва бошқарув},
  year = {2025},
  volume = {2025},
  number = {4},
  pages = {81-89},
  doi = {10.59048/2181-1105.1701},
  url = {https://doi.org/10.59048/2181-1105.1701}
}

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