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Evaluation of Traffic Sign Recognition Methods Trained on Synthetically Generated Data
P. 576–583.
Moiseyev B., Konev A., Chigorin A., Konushin A.
Most of today’s machine learning techniques requires large manually labeled data. This problem can be solved by using synthetic images. Our main contribution is to evaluate methods of traffic sign recognition trained on synthetically generated data and show that results are comparable with results of classifiers trained on real dataset. To get a representative synthetic dataset we model different sign image variations such as intra-class variability, imprecise localization, blur, lighting, and viewpoint changes. We also present a new method for traffic sign segmentation, based on a nearest neighbor search in the large set of synthetically generated samples, which improves current traffic sign recognition algorithms.
Language:
English
In book
Vol. 8192: Advanced Concepts for Intelligent Vision Systems. , Springer, 2013.
Pikul A. S., Безопасность информационных технологий 2024 Т. 31 № 4 С. 116–127
This article explores the potential use of modern computer vision architectures for the task of deepfake detection. The following architectures are considered: EfficientNet, Vision Transformer (ViT), VisionLSTM (ViL), Vision KAN, and Mamba Vision. The novelty of the approach lies in the application and comparison of these architectures, as well as their combination into paired ensembles ...
Added: December 12, 2025
[б.и.], 2026.
The Winter Conference on Applications of Computer Vision is the premier international computer vision event comprising the main conference and several co-located workshops and tutorials. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. ...
Added: November 24, 2025
Dalian: IEEE, 2025.
The increasing complexity of modern software development necessitates intelligent, automated security analysis frameworks that can effectively pay attention of human on high-risk software releases. This paper introduces a Multi Agent System (MAS) framework designed to enhance the security assessment process by leveraging artificial intelligence (AI) and intelligent computing for real-time release analysis. The proposed system ...
Added: November 3, 2025
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Added: October 1, 2025
Slastnikov S., Petr Rybakov, Matvei Antonov et al., , in: 24th International Conference, NEW2AN 2024, and 17th Conference, ruSMART 2024, Marrakesh, Morocco, December 11–12, 2024, Proceedings, Part I. Internet of Things, Smart Spaces, and Next Generation Networks and Systems. (LNCS, volume 15554)* 1.: Cham: Springer, 2025. P. 11–18.
An algorithmic and architectural solution is presented for the Search And Rescue (SAR) problem in open areas using image data from UAVs in real time. The solution is an original software and hardware complex that includes a UAV, on-board deployed machine vision application with a novel object detection model and a transmitter to send coordinates ...
Added: June 11, 2025
Springer, 2025.
The multi-volume set LNCS 15623 until LNCS 15646 constitutes the proceedings of the workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024, which took place in Milan, Italy, during September 29–October 4, 2024.
These LNCS volumes contain 574 accepted papers from 53 of the 73 workshops. The list of ...
Added: June 1, 2025
Generation of Artificial Images of Cross Sections of WC/Co Composite Alloys Using Diffusion Networks
Kagramanyan D., Shchur L., Lobachevskii Journal of Mathematics 2025 Vol. 46 No. 3 P. 1315–1321
The study of statistical properties of microstructures of composite materials is carried out by analysing microphotographs of material cuts. The obtained two-dimensional structure represents cuts of composite elements, the geometrical properties of which can be studied by computer vision methods. Hundreds of micro-images can be collected from one microstructure, which allows to study statistical properties ...
Added: January 13, 2025
Springer, 2024.
Added: December 9, 2024
Kseniia Prokudina, Mikhail Skriplyonok, Alexander Vostrikov, , in: 2024 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), 20-24 May 2024.: IEEE, 2024. P. 865–869.
Added: November 26, 2024
Springer, 2024.
The multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024.
The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal ...
Added: November 26, 2024
Sergeev A., Minchenkov V., Солдатов А. В. et al., / Cornell University. Серия Computer Science "arxiv.org". 2024. № 2411.10150.
Various technologies, including computer vision models, are employed for the automatic monitoring of manual assembly processes in production. These models detect and classify events such as the presence of components in an assembly area or the connection of components. A major challenge with detection and classification algorithms is their susceptibility to variations in environmental conditions ...
Added: November 23, 2024
Cham: Springer, 2024.
This multi-volume set, LNAI 14941 to LNAI 14950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2024, held in Vilnius, Lithuania, in September 2024. ...
Added: November 22, 2024
Prutyanov V., Тернов М. А., Костров Д. С., , in: 2024 International Russian Automation Conference (RusAutoCon).: IEEE, 2024.
The work presents an analysis of the application of deep learning-based methods for the keypoint extraction and matching in the context of map-aided UAV visual localization. A method for visual localization in three degrees of freedom is proposed, which employs pretrained SuperPoint and LightGlue networks for both short-term optical flow and global frame-to-map matching components. ...
Added: September 12, 2024
Проворова А. А., Polyakova I., Kuzmicheva E., Научная визуализация 2024 Т. 16 № 3 С. 1–13
Machine methods of image analysis are gaining popularity in various fields of life. However, the question remains as to how effective such algorithms are on low-quality data, such as those that can be used in the field of telemedicine. The work provides a comparative analysis of various approaches to object detection in MRI brain images ...
Added: August 9, 2024
IEEE, 2024.
Added: July 10, 2024
Meshcheryakov R., Kataev M., Pantiukhin D., , in: Integral Robot Technologies and Speech Behavior.: Newcastle upon Tyne: Cambridge Scholars Publishing, 2024. Ch. 4 P. 130–154.
For a robot that functions and performs its mission, it is important to receive information from various sources that are increasingly being called sensors. When drawing an analogy between a robot and a living being, the same comparison can be made for obtaining information about the world from various sources. Often, a computer vision system ...
Added: December 10, 2023
Kharlamov A. A., Pantiukhin D., Borisov V. et al., Newcastle upon Tyne: Cambridge Scholars Publishing, 2024.
The monograph presents papers on the subject domain “Integral robot. Speech behavior”. These cover issues of a theoretical nature, including representation and processing of speech information in the human mind in the process of both text analysis and text generation, and specifically the need to use jointly working linguistic and extralinguistic models of the world, ...
Added: December 1, 2023
Cham: Springer, 2023.
Added: November 29, 2023
Berezovskiy V., Morozov N., , in: The 2nd Workshop and Challenges for Out-of-Distribution Generalization in Computer Vision. ICCV 2023.: [б.и.], 2023.
Knowledge distillation (KD) is a powerful model compression technique broadly used in practical deep learning applications. It is focused on training a small student network to mimic a larger teacher network. While it is widely known that KD can offer an improvement to student generalization in i.i.d setting, its performance under domain shift, i.e. the ...
Added: November 20, 2023
The 2nd Workshop and Challenges for Out-of-Distribution Generalization in Computer Vision. ICCV 2023
[б.и.], 2023.
Deep learning models are usually developed and tested under the implicit assumption that the training and test data are drawn independently and identically distributed (IID) from the same distribution. Overlooking out-of-distribution (OOD) images can result in poor performance in unseen or adverse viewing conditions, which is common in real-world scenarios. In this workshop, we are ...
Added: November 20, 2023
IEEE, 2023.
Computer Vision (ICCV), 2023 IEEE International Conference on ...
Added: November 5, 2023
Искандеров Ю. М., Катарушкин Б. Е., Ершов А. А., Информатизация и связь 2020 № 2 С. 46–51
Aim. Currently, when creating intelligent information systems in various fields of practical activity, machine learning methods are used. The article shows the possibilities of using these methods in automating the detection of obstacles in the interest of improving safety and reducing the number of emergencies at level crossings. Materials and methods. The article discusses advanced ...
Added: September 15, 2023