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• MODERN APPROACHES TO FACE RECOGNITION IN LOW-LIGHT CONDITIONS: A REVIEW AND THE CONCEPT OF A HYBRID END-TO-END ARCHITECTURE

  D. А. Morozov , V.V. Gilka , А. S. Kuznetsova

  113-133

  2026-07-07

  Abstract ▼

  The article addresses the problem of reliable face recognition in critical areas such as video surveillance and biometric authentication under low-light conditions. Existing approaches typically separate the tasks of image enhancement and face identification, which leads to error accumulation and loss of informative features. The aim of this work is to overcome this limitation by developing and theoretically substantiating a hybrid end-to-end architecture in which image enhancement and face recognition are solved jointly. The study provides a systematic review of modern methods, including classical algorithms (such as histogram equalization and noise suppression) and advanced deep neural networks (including EnlightenGAN, Zero-DCE, ArcFace, and RetinaFace). The main contribution is the integration of generative and identification modules into a single computational graph. The key result of the study is the demonstration that joint optimization of all processing stages within a unified model, unlike fragmented solutions, fundamentally changes the approach to the problem. Theoretical analysis and comparative evaluation of existing concepts show that the proposed architecture ensures a more efficient gradient flow during training, leading to the formation of higher-quality and noise-robust identity features. It is shown that this approach prevents error accumulation between stages and minimizes information loss. The novelty of the work lies in the holistic, end-to-end view of the face recognition problem under low-light conditions. The practical significance is confirmed by the applicability of the architecture in real systems, where its implementation can potentially improve reliability and processing speed by combining heterogeneous tasks into a single optimizable framework.

  The article addresses the problem of reliable face recognition in critical areas such as video surveillance and biometric authentication under low-light conditions. Existing approaches typically separate the tasks of image enhancement and face identification, which leads to error accumulation and loss of informative features. The aim of this work is to overcome this limitation by developing and theoretically substantiating a hybrid end-to-end architecture in which image enhancement and face recognition are solved jointly. The study provides a systematic review of modern methods, including classical algorithms (such as histogram equalization and noise suppression) and advanced deep neural networks (including EnlightenGAN, Zero-DCE, ArcFace, and RetinaFace). The main contribution is the integration of generative and identification modules into a single computational graph. The key result of the study is the demonstration that joint optimization of all processing stages within a unified model, unlike fragmented solutions, fundamentally changes the approach to the problem. Theoretical analysis and comparative evaluation of existing concepts show that the proposed architecture ensures a more efficient gradient flow during training, leading to the formation of higher-quality and noise-robust identity features. It is shown that this approach prevents error accumulation between stages and minimizes information loss. The novelty of the work lies in the holistic, end-to-end view of the face recognition problem under low-light conditions. The practical significance is confirmed by the applicability of the architecture in real systems, where its implementation can potentially improve reliability and processing speed by combining heterogeneous tasks into a single optimizable framework.

• MODERN APPROACHES TO NATURAL FIRE MONITORING AND FORECASTING: REVIEW AND CONCEPT OF AUTONOMOUS UAV-BASED SYSTEM

  N.D. Boldyrev , V. V. Gilka , А.S. Kuznetsova , D.А. Morozov

  58-80

  2025-12-30

  Abstract ▼

  Natural fires cause serious damage to ecosystems, the economy, and public safety every year, and timely detection of fires and prediction of their development increases the speed of response to threats and allows for optimal allocation of resources during emergency response. Existing monitoring methods are limited by the speed of detecting fire outbreaks and the speed of their further spread, which reduces the effectiveness of rescue services. To solve this problem, heterogeneous data sources can be used, including unmanned aerial vehicles (UAVs), distributed sensor networks, mobile field observation systems, ground-based thermal imaging stations, etc., which can contribute to a more accurate analysis of the current situation and improve the reliability of predictive models of fire spread. The aim of the study was to develop a concept for an automated approach to monitoring and predicting wildfires based on unmanned aerial vehicles. We believe that this approach will improve the speed of detecting fire outbreaks and the accuracy of predicting their spread. The tasks include analyzing existing monitoring methods, developing a concept for a system that integrates multispectral imaging, optimized data transmission, automatic segmentation, and forecasting based on machine learning, as well as ensuring interaction between the operator and alert specialists. The work used methods of collecting, analyzing, and transmitting data from UAVs, processing multispectral images, machine learning and neural networks for fire detection, image segmentation algorithms and simulation modeling for fire spread prediction, data visualization to support decision-making by operators and administrators, logging and analysis of results for model training, software engineering, and human-computer interaction technologies. The system will reduce the time required to detect and predict fires, enable operators to launch multiple drones simultaneously, and automate the processing of data received from them. Process automation will reduce emergency response times and staffing levels, improve resource allocation, increase forecast accuracy, and improve the timeliness of emergency service notifications. This will help reduce damage from wildfires and improve the safety of people and ecosystems. Despite the progress made in addressing this challenge, the comprehensive system described in this article does not yet exist in its entirety in Russia, the CIS countries, or in Western and Asian countries. Although individual components, such as UAVs for monitoring and artificial intelligence (AI) for data analysis, are already in active use, there is currently no integrated solution that combines all elements (drone control, near real-time fire spread prediction, data transmission, and interaction with emergency services). does not currently exist. This concept represents a new approach that could become a breakthrough technology for combating natural disasters.

• VERIFICATION OF THE MODEL AND METHOD FUNCTIONALITY FOR REMOTE HEALTH MONITORING ILLUSTRATED BY THE DEVIATIONS IN HUMAN BODY TEMPERATURE INDICATORS

  V.V. Gilka, А.S. Kuznetsova, J.F. El-Ait, А.А. Moldovskaya

  2023-12-11

  Abstract ▼

  The rapid development of telemedicine in the healthcare sector facilitates the active implementation
  of various methods and models for remote monitoring of human health indicators. In
  this regard, significant attention is paid to the development of mobile applications capable of
  providing accurate and timely monitoring of key health indicators in real-time. The aim of this
  research is to evaluate the applicability of the proposed method and model for remote monitoring
  of human health indicators, and to analyze the effectiveness of the developed mobile application
  HelpMeTracker in identifying deviations in human body temperature indicators. To assess the
  functionality of the proposed health monitoring method, a comprehensive experiment was conducted,
  which included participants from different age categories and social groups. During the
  experiment, the application was to analyze and track the dynamics of changes in human body temperature
  indicators using sensors integrated into smartwatches or fitness trackers, and timely
  notify about detected anomalies for the possibility of rapid response to changes in health status. The results obtained during the conducted research demonstrate that the proposed model and
  method for remote monitoring of human health indicators have a high degree of effectiveness for
  observing the current health status. During the experiment, the HelpMeTracker application reliably
  detected all deviations in body temperature indicators obtained from sensors of wearable devices
  and successfully informed all participants of the process. Based on the obtained results, it
  can be concluded that the application of the proposed approach for remote monitoring is sufficient
  to capture deviations in body temperature indicators, track the dynamics of changes, and form a
  substantiated comprehensive assessment of human health based on the information received from
  the device sensors.