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• IMAGE RECOGNITION OF AGRICULTURAL CROPS, PLANTS AND FORESTS

  I. B. Abbasov, Ratnadeep R. Deshmukh

  2020-10-11

  Abstract ▼

  The paper provides an overview of some studies on the recognition of images of crops,
  plants and forests. These image recognition systems use various methods of pre-processing, computer
  vision, and deep learning. Recently recognition systems based on mobile devices are increasing,
  which increases their availability and wide distribution. The articles on recognition,
  classification of fruits and fruits in orchards, the creation of a data bank of these agricultural
  products (apples, pears, kiwi) to assess ripening and yield are considered. The works devoted to
  the automation of harvesting grain crops are described on the example of the work of a combine
  harvester using machine vision. Crop production plays an important role in providing feed for
  animal husbandry; articles on the recognition of agricultural plants based on leaf images are
  analyzed. Also, by the condition of the leaves of potato bushes, you can determine their disease,
  assess the condition of the soil. The work on the development of mobile systems for monitoring and
  recognition of the process of growing mushrooms based on the "green house" technology for
  farms is presented. Using remote diagnostics, you can analyze and monitor the state of the surface
  of land and seas. For remote environmental monitoring of the landscape of the earth's surface,
  work is described on the recognition, classification of forests, water resources using hyperspectral
  analysis of satellite images.

• 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.

• ALGORITHM FOR PRE-PROCESSING VIDEO IMAGES TO INCREASE THE ACCURACY OF SMALL OBJECT DETECTION

  V.V. Kovalev, N.E. Sergeev

  2021-12-24

  Abstract ▼

  Recognition of certain patterns in video images captured by a camera is carried out using
  training methods based on convolutional neural networks. The larger the number of images with
  multiple features and the more diverse the training sample of video images, the better the convolutional
  neural networks extract features from the sequence of video images that were not included in
  the training sample. This is a consequence of increasing the accuracy of detecting visual images on
  video images containing features of target images. However, there are limitations in improving the
  detection performance when the size of the image to be detected is much smaller than the background
  area, or when the image is described with little information. To solve problems of this kind, the authors
  of the article have developed an algorithm for the spatio-temporal integration of information
  about the movement of dynamic images. The algorithm processes a fixed number of video images at
  certain points in time and extracts new independent signs of motion of dynamic images based on
  space-time processing of video images. Further, it combines new local motion features with the original
  video image features. This allows you to add a motion feature of dynamic images while preserving
  the original image features that describe static images. Areas of the video image that characterize
  the motion feature are displayed in a «color» cluster. The use of pre-processing is aimed at improving the accuracy of pattern detection, provided there are dynamic visual images on a static background.
  If the camera is in scan mode, a static background can be provided with a video stabilizer.
  Experimentally, estimates of integral criteria for the accuracy of detection neural network algorithms
  have been obtained, showing an increase in the accuracy of detecting visual images using
  the algorithm for spatial-temporal integration of motion information.

• REALTIME NEURAL NETWORK ALGORITHM FOR FULL-FRAME MARINE SURFACE OBJECTS RECOGNITION

  V.A. Tupikov, V.A. Pavlova, V.A. Bondarenko, N.G. Holod

  2020-07-10

  Abstract ▼

  The article explores modern neural network architectures for the automatic detection and recognition of marine surface objects and obstacles of given classes throughout the full image area, applicable for execution in real or near real time on an optoelectronic vision system to au-tomate and improve the safety of civil marine navigation. A formal statement of the problem of automatic detection of objects on images is given. The state-of-the-art algorithms for detecting objects in images based on use of artificial convolutional neural networks were reviewed, their comparison was made and a reasonable choice was made in favor of the most efficient neuralnetwork architecture in terms of computational complexity to recognition accuracy. The subject area is studied, as well as publicly available databases of surface objects suitable for use in the training of algorithms using artificial neural networks. The article concluded that there is insuffi-cient labeled data for training neural network algorithms, as a result of which the authors inde-pendently collected research images and video sequences, prepared and labeled the collected data containing surface marine objects and other obstacles that represent a navigation hazard for ships. Based on the selected neural network architecture, a new neural network algorithm for automatic full-frame detection and recognition of surface objects was developed, and an artificial neural network was trained using the prepared database of images of typical objects. The resulting algorithm was tested by the authors on a validation data set, the quality of its work was estimated using various metrics, and the algorithm’s performance was measured. Conclusions are made about the necessity to expand the collected database of images of typical marine objects, further steps are proposed to improve the accuracy of the developed software and algorithmic complex and its implementation to be used in a marine optoelectronic machine vision system for automa-tion and improving the safety of civil navigation.

• DISTRIBUTED SYSTEM FOR BARCODE RECOGNITION USING NEURAL NETWORKS

  А.Y. Yurchenko , М.Y. Polenov

  70-79

  2025-10-01

  Abstract ▼

  This work presents a distributed software-hardware system for automated barcode recognition on moving objects in industrial environments. The primary objective of the research is to develop a reliable and adaptive solution capable of consistently reading barcodes regardless of the orientation, speed, or height of objects moving along a conveyor belt. The main focus is not on achieving maximum processing speed, but rather on providing a wide field of view and ensuring reliable recognition of moving objects. Unlike traditional scanners that require precise positioning and expensive hardware, the proposed approach leverages a single network camera and a server equipped with neural processing modules, providing a cost-effective and versatile alternative suitable for a wide range of industrial applications. A key component of the system architecture is a neural image restoration module based on the MPRNet model, which effectively reduces motion blur and optical distortions in video frames. After preprocessing, frames are passed to an object detection module built upon the YOLO architecture, which has been adapted specifically for barcode recognition. Detected barcode data is stored in a database using an ORM interface, enabling seamless integration with existing enterprise systems. To prevent frame loss and maintain high throughput, the system incorporates asynchronous processing mechanisms using multithreading and buffered queues. The relevance of this research stems from the widespread use of barcodes as the primary method of product marking in industrial settings and the increasing demand for automation in product tracking and inventory control. Despite the availability of various vision-based and scanning solutions, most existing systems are not designed to handle unstable or low-quality video streams. The proposed system demonstrates robustness to visual distortions and motion-related artifacts, making it suitable for deployment in real production environments. Its affordability and adaptability also open up possibilities for implementation in logistics, warehousing, and supply chain management.