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Improving the Accuracy of One-Shot Detectors for Small Objects in X-ray Images
Ch. 110. P. 610–614.
In this paper, we address the problem of detecting small objects on high-quality X-ray imagesusing deep neural networks. We propose to implement the two-stage approach, in which, firstly, input image issplit into partially overlapping blocks to make small objects more discriminative for detection. Secondly, the small blocks are fed into conventional single-shot detectors. These detectors are trained using the blocks of the training images extracted by the same procedure.Two datasets of X-ray images from the customs inspection complex are examined in the experimental study. It was shown thatthe proposed algorithm with data augmentationleads tomore precise results when compared to the conventional technique:ourmethod outperforms the traditional approach by 5.4 - 25.7% depending on the type of used backbone convolutional neural network.
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
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
Grigoriy Simakov, Stepanyants V., Martyusheva A., , in: 2025 International Russian Smart Industry Conference (SmartIndustryCon).: Sochi: IEEE, 2025. P. 794–798.
The development of intelligent transportation systems, and intelligent vehicles in particular, requires significant time and effort, with one of the primary phases being the training of the automated driving system. This training can be conducted using real-world annotated data or within a simulation environment. Simulation tools can provide a wide array of traffic scenarios unavailable ...
Added: May 10, 2025
, in: Lecture Notes in Electrical EngineeringVol. 489: Applied Physics, System Science and Computers II.: Springer, 2019. P. 87–100.
To give shift in safety protocols, we have employed advanced deep learning algorithms and frameworks to construct an innovative AI model. The designed model detects the usage of personal protective equipment (PPE) by workers in high-risk industries such as construction and manufacturing. We have used Google’s TensorFlow object detection API to modify and train a model for ...
Added: December 30, 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
Kulyasova E. V., Kulyasov N.S., Puchkov A. Y., , in: Journal of Physics: Conference Series Volume 1260, 2019 Mechanical Science and Technology Update 23–24 April 2019, Omsk, Russian Federation.: IOP Publishing, 2019. Ch. 3 P. 032024–032024.
This article is introduced into the perspective tendencies of the digital transformation of chemical enterprises which allow to improve the process of managing enterprises of the branch. Presented the algorithms of managing and technological information processing based on deep neural network apparatus. New approaches to data processing known as video analytics are applied; it allows ...
Added: September 27, 2024
Ryzhikov A., Hushchyn M., Derkach D., IEEE Access 2023 Vol. 11 P. 104700–104711
Automated analysis of complex systems based on multiple readouts remains a challenge. Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process. In this paper, we present a novel change point detection algorithm based on Latent Neural Stochastic Differential Equations (SDE). Our method learns a non-linear deep ...
Added: October 5, 2023
Grigoryev T., Verezemskaya P., Krinitskiy M. et al., Remote Sensing 2022 Vol. 14 No. 22 Article 5837
Global warming has made the Arctic increasingly available for marine operations and created a demand for reliable operational sea ice forecasts to increase safety. Because ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient for sea ice forecasting. Many studies have exploited different deep learning models alongside classical approaches ...
Added: June 19, 2023
Nakhodnov M., Kodryan M., Lobacheva E. et al., , in: Doklady MathematicsVol. 106. Issue 1: Supplement.: Pleiades Publishing, Ltd. (Плеадес Паблишинг, Лтд), 2023. P. 43–62.
Knowledge of the loss landscape geometry makes it possible to successfully explain the behavior of neural networks, the dynamics of their training, and the relationship between resulting solutions and hyperparameters, such as the regularization method, neural network architecture, or learning rate schedule. In this paper, the dynamics of learning and the surface of the standard ...
Added: June 9, 2023
Сучков Е. П., Алексеенко Г. О., Налчаджи К. В., Интеллектуальные системы. Теория и приложения 2022 Т. 26 № 1 С. 250–254
Currently, video surveillance systems are becoming more
widespread. One of the main goals of such systems is to control and
track a person’s movement. The solution of this problem allows us
to solve such applied problems as tracking the occupancy of various
premises (whether shopping facilities or educational and cultural
institutions), creating a motion heatmap or organizing control of access
to ...
Added: January 31, 2023
Алексеенко Г., Налчаджи К., Интеллектуальные системы. Теория и приложения 2022 Т. 26 № 1 С. 250–254
В настоящее время все более широкое распространение получают различные системы видеофиксации. Одной из основных целей таких систем является контроль и слежение за человеком. Решение данной задачи позволяет в дальнейшем решать такие прикладные задачи, как контроль заполненности различных помещений (будь-то торговые объекты или образовательно-культрурные учереждения), построение тепловой карты перемещений человека, организация контроля доступа к тому или ...
Added: December 21, 2022
Kodryan M., Lobacheva E., Nakhodnov M. et al., , in: Thirty-Sixth Conference on Neural Information Processing Systems : NeurIPS 2022.: Curran Associates, Inc., 2022. P. 14058–14070.
A fundamental property of deep learning normalization techniques, such as batch normalization, is making the pre-normalization parameters scale invariant. The intrinsic domain of such parameters is the unit sphere, and therefore their gradient optimization dynamics can be represented via spherical optimization with varying effective learning rate (ELR), which was studied previously. However, the varying ELR ...
Added: December 20, 2022
Rukhovich D., Koroleva P., Rukhovich D. et al., Remote Sensing 2022 Vol. 14 No. 9 Article 2224
The detection of degraded soil distribution areas is an urgent task. It is difficult and very time consuming to solve this problem using ground methods. The modeling of degradation processes based on digital elevation models makes it possible to construct maps of potential degradation, which may differ from the actual spatial distribution of degradation. The ...
Added: November 14, 2022