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Real-Time Vehicle Type Detection and Counting from Road Camera Video
P. 92–98.
Denis Zuenko, Makarov I.
In this paper, we study automatic recognition and counting of vehicles in the wild. For this problem, we tested several object detection models for car type recognition among five classes: Bicycle, Bus, Car, Motorcycle, Truck, Van. We extend existing dataset in order to balance classes and achieve classification quality for detected cars with 92% mAP
In book
Vol. 2795. , CEUR Workshop Proceedings, 2020.
Aleksei Samarin, Nazarenko A., Nazarenko D. et al., Machine Learning and Knowledge Extraction 2026 Vol. 8 No. 10 Article 330
Automated microscopic image analysis is essential for quantitative microbiology, yet manual acquisition and generic recognition models struggle with morphological variability and scene artifacts. We address both ends of this pipeline. First, we present a low-cost, open-source acquisition system, based on an Arduino UNO R3 microcontroller and NEMA 17 stepper motors, that automates focusing, magnification, and ...
Added: October 14, 2026
Piskunova A., Korolev D., Скопин Н. Ю. et al., В кн.: ГрафиКон 2025 : материалы 35-й Международной конференции по компьютерной графике и машинному зрению (Россия, Йошкар-Ола, 30 сентября – 2 октября 2025 г.).: Йошкар-Ола: Поволжский государственный технологический университет, 2025. С. 659–669.
This article presents an overview of modern methods for solving the problem of recognizing handwritten content, including multilingual text (Russian and English) and graphical elements such as block diagrams consisting of rectangles and arrows, with a focus on the recognition of whiteboard images. For multilingual handwritten text, architectures capable of processing multiple languages are considered, ...
Added: October 9, 2026
Groshev Maksim, Rybakov Petr, Teterin N. et al., Sensors 2026 Article 6171
Manual assessment of soybean yield components, such as pod number, is laborious, time-consuming, and subjective. Existing computer-vision approaches based on object detection or instance segmentation perform poorly on close-range RGB imagery of soybean canopies due to severe occlusions, ambiguous plant boundaries, and the high cost of bounding-box annotation. To address these challenges, we propose a ...
Added: October 7, 2026
Aleksei Samarin, Nazarenko A., Alexander Savelev et al., Pattern Recognition and Image Analysis 2024 Vol. 34 No. 3 P. 844–854
Improving image quality is becoming an increasingly popular task, especially when working with mobile devices. One common approach to image enhancement is the use of convolutional neural networks. However, to achieve good results, such networks must be large enough, otherwise there is a risk of unwanted artifacts. In addition, large convolutional neural networks require significant ...
Added: September 21, 2026
Самарин А. В., Торопов А. Г., Савельев А. Г. et al., Pattern Recognition and Image Analysis 2024 Vol. 34 No. 4 P. 1044–1052
This paper presents a study aimed at improving the detection quality of small-sized microorganisms under challenging microscopic conditions through the application of a lightweight combined image preprocessing model. We focused on the task of detecting diplococci in images obtained through dynamic sample microscopy. The proposed approach employs predefined filters for image preprocessing, combined with the ...
Added: September 21, 2026
Aleksei Samarin, Aleksei Toropov, Alexander Savelev et al., , in: Pattern Recognition. ICPR 2024 International Workshops and Challenges. Kolkata, India, December 1, 2024, Proceedings, Part III. LNCS, volume 15616.: Cham: Springer, 2025. P. 308–320.
This research explores an innovative approach to enhancing the accuracy of detecting small microorganisms in complex microscopic environments. Our study introduces a streamlined, hybrid image pre-processing model specifically designed to address the challenges of identifying diplococci in live microscopy of dynamic samples. By integrating pre-defined filtering techniques with predictive adjustments for optimal applicability, our method ...
Added: September 21, 2026
Cham: Springer, 2026.
computer vision ...
Added: September 19, 2026
Aleksei Samarin, Aleksei Toropov, Dzestelova A. et al., , in: Proceedings of the 36th Conference of Open Innovations Association FRUCT, Helsinki, Finland, 30 October - 1 November 2024Vol. 36.: FRUCT Oy, 2024. P. 903–911.
In this work, we describe our research aimed at developing classifiers for microbial images (bacilli images) obtained through microscopy of live (non-static) samples. We employed our proposed approach called AutoML, which is based on the automatic generation and analysis of the feature space to create the most optimal descriptors for microscopic images used in their ...
Added: September 19, 2026
FRUCT Oy, 2024.
Added: September 19, 2026
Aleksei Samarin, Aleksei Toropov, Kotenko E. et al., , in: Proceedings of the 37th Conference of Open Innovations Association FRUCT, Helsinki, Finland, 14-16 May 2025, Issue 1 (Full Papers)Vol. 37.: Helsinki: FRUCT Oy, 2025. P. 278–284.
This study introduces an innovative method for recognizing automatically generated images by utilizing adapted descriptors specifically designed to analyze unique structural and morphological features characteristic of artificially created content. The methodology focuses on analyzing features inherent to image generation processes, ensuring the optimization of descriptors for identifying complex and subtle patterns associated with generative algorithms. ...
Added: September 19, 2026
Helsinki: FRUCT Oy, 2025.
Added: September 19, 2026
Helsinki: FRUCT Oy, 2026.
Added: September 19, 2026
Lebedev O. B., Черкасов Р. И., Известия ЮФУ. Технические науки 2025 № 5(247) С. 254–276
This paper considers the application of artificial intelligence technologies, in particular computer vision, in visual information processing systems. A comprehensive analysis of neural network approaches to solving computer vision problems is carried out, including systematization of key types of problems: image classification, object detection and semantic segmentation. The architectural principles of convolutional neural networks are ...
Added: September 10, 2026
Denis Zuenko, Khaidarova I., Trofimova E., IEEE Access 2026 Vol. 14 P. 121339–121357
Oil spill segmentation in Synthetic Aperture Radar (SAR) images is limited by noisy annotations in publicly available datasets and by architectural choices that interact with label quality in opposing directions. First, we introduce a manually refined version of the Deep-SAR Oil Spill (SOS) dataset, in which 36.25% of masks are corrected for false positives, missed ...
Added: September 7, 2026
Kaznacheev P. A., Indakov G. S., Podymova N. B. et al., Izvestiya - Physics of the Solid Earth 2026 Vol. 61 P. 68–80
The paper discusses methods for estimating the grain size of polycrystalline materials based on the analysis of optical microscopic images, as well as the possibility of applying these methods to the analysis of rock textures. A comparison of the results obtained using several methods on a single sample of metamorphosed sandstone from the Northern Ladoga ...
Added: August 31, 2026
Gamberov T., Safin R., Chebotareva E. et al., , in: 2025 9th International Conference on Information, Control, and Communication Technologies (ICCT-2025).: IEEE, 2025. P. 1–5.
Digital human models (DHM) for virtual environments allow a cost-effective and reproducible evaluation of computer vision algorithms that are employed in robotics, e.g., human detection, tracking and following. Yet, many popular robotics simulators do not achieve a reasonable level of realism and diversity required to rigorously test such algorithms under varying conditions. This paper introduces ...
Added: June 23, 2026
Slivnitsin P., Mylnikov L., Engineering Applications of Artificial Intelligence 2026 Vol. 179 Article 115185
The paper describes a applied artificial intelligence task of recognition-by-components method of real objects based on the recognition of a limited set of primitives or components. The recognition-by-components makes it possible to determine the components, that compose an object, and increase the number of recognizable objects without degrading the recognition quality. Training is performed on ...
Added: May 29, 2026
Vasilev A., Kapitanov A., Roman Solovyev et al., PeerJ Computer Science 2026 Vol. 12 Article 3724
This article introduces MinMAE, a novel activation calibration method for Post-Training Quantization (PTQ) that significantly reduces accuracy loss in Convolutional Neural Networks (CNN). Motivated by the need for high-fidelity quantization without costly retraining, MinMAE directly minimizes the Mean Absolute Error (MAE) between original and dequantized activations, making it robust to outliers that degrade standard methods. ...
Added: May 3, 2026
Karpukhin I., Shipilov F., Savchenko A., Neurocomputing 2026 Vol. 672 Article 132771
Forecasting multiple future events within a given time horizon is essential for applications in finance, retail,
social networks, and healthcare. This problem is typically addressed using Marked Temporal Point Processes
(MTPP), which provide a principled framework for modeling both event timing and event labels. While most
existing research focuses on predicting only the next event, forecasting distant future ...
Added: February 25, 2026
Moshkin A., Лапутин Ф. А., Сидоров И. В., DIGITAL DIAGNOSTICS 2024 Т. 5 № S1 С. 40–42
BACKGROUND: Ovarian reserve reflects a woman's ability to successfully realize reproductive function. The assessment of ovarian reserve is an urgent task for clinical practice [1] and is important in scientific research. The use of computerized diagnostic image processing methods can accelerate and facilitate the performance of routine tasks in clinical practice. Their use in retrospective ...
Added: February 21, 2026
Йошкар-Ола: Поволжский государственный технологический университет, 2025.
Представлены материалы 35-й Международной конференции «ГрафиКон 2025», проходившей на базе Поволжского государственного технологического университета.
В сборник вошли доклады участников конференции, посвященные методам и технологиям компьютерного анализа изображений, визуальной и когнитивной аналитики, 3D-реконструкции, визуальной навигации и человеко-машинного взаимодействия, виртуальной и дополненной реальности, распознавания образов и др.
Издание адресовано сотрудникам научно-исследовательских и образовательных организаций, специалистам предприятий ИТ-индустрии, аспирантам, студентам. ...
Added: February 21, 2026