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• PROSPECTS OF MALE-CLASS UAVS USING FOR THE HUGE TERRITORIES AERIAL SURVEY

  А. М. Fedulin, D.M. Driagin

  2021-04-04

  Abstract ▼

  The aim of the study is to estimate the MALE-class (Medium Altitude Long Endurance) UAV
  (Unmanned Air Vehicles) using possibility to solve the problem of regular aerial survey of huge
  areas relative to other means used for this, such as: small-sized UAVs, satellite remote sensing
  and manned aircrafts. Considered is the issue of practical construction of onboard computer vision
  system based on a UAV “Orion” wit a ta eoff weig t of more t an a ton, w ic pro ides
  aerial photography in the visible and near infrared range and airborne laser scanning of the underlying
  surface with automatic processing of the received data on board in near real-time mode
  detecting the changes occurred since the previous survey. It has been determined the key components
  of the computer vision system both the hardware and software platform required highperformance
  computing and big-data storage. It has been presented a promising architecture,
  given estimates for its search performance, weight and power consumption, determined the typical
  flight altitude, which provides the input data spatial resolution, which is necessary for objectoriented
  change detection algorithms, based on a convolutional neural networks machine learning.
  It has been proposed organizational and technical solutions to speed up the data processing
  cycle, taking into account the requirements of the legislation regarding the declassification of
  aerial survey data. The results obtained confirm that after the issuance of the Orion UAV by the
  Federal Air Transport Agency of the aircraft type certificate, which gives the right to perform
  commercial flights in the shared airspace of the Russian Federation, it will be possible to implement
  an aerial survey complex of high productivity and degree of autonomy using cut of the edge
  CV & ML technologies. It seems the tactical, technical and economic capabilities of which proposed
  will be orders of magnitude superior to the currently existing solutions especially for hardto-
  reach regions.

• AN INTELLIGENT SYSTEM OF TECHNICAL VISION FOR DETECTING OBSTACLES AND PREDICTING THE BEHAVIOR OF MOVING OBJECTS ON RAILWAY TRACKS

  D.L. Shishkov, М.N. Zaripov, R.А. Gorbachev

  2022-04-21

  Abstract ▼

  Currently, the improvement of the quality of transport and logistics services provided is directly
  related to the introduction of new and modernization of existing technologies of
  informatization and digitalization. One of the most urgent tasks solved by the introduction of digital
  technologies into existing technological processes is to improve the safety of train traffic.
  The analysis of domestic and foreign works devoted to the development of train safety improvement
  systems has shown that one of the methods of solving the task is the development and implementation
  of vision systems for detecting infrastructure objects and obstacles in the course of train
  movement. This is especially true when train speeds increase when it is difficult for the driver to
  correctly assess the current situation and make an operational decision. This paper describes the
  implementation of a vision system for unmanned trains. Within its framework, a new approach to
  the training of a highly specialized mask neural network was implemented. The main task of this
  system is to recognize obstacles and human figures against the background of the railway infrastructure
  determine their location relative to the tracks and assess this situation from the point of
  view of traffic safety. To obtain a higher-quality mask, the approach of simultaneous use of images
  of standard CVS cameras and cameras with the higher resolution was used. This method is able toimprove the quality of recognition, especially at large distances, when the object of interest is not
  noticeable in the complex environment surrounding it. The work performed has shown good results
  in identifying objects on railway tracks. The creation of a prototype of such a system and
  equipping it with traction rolling stock will allow for the timely detection of obstacles and people
  on the train path, which contributes to improving the level of train safety.

• METHODOLOGICAL SUPPORT FOR ASSESSING THE AVAILABILITY OF GOODS IN DISTRIBUTED STORAGE BASED ON COMPUTER VISION METHODS

  А.R. Nedvigin , R.М. Sinetsky

  2026-02-27

  Abstract ▼

  This paper presents a formalization of the problem of automated monitoring of product availability on retail shelves and compliance with the prescribed planogram, leveraging computer vision and machine learning techniques. The aim of this research is to develop algorithmic solutions for the automatic assessment of product availability in distributed retail environments using computer vision methods, thereby addressing the challenge of maintaining optimal and necessary product assortments through continuous shelf monitoring and supporting data-driven managerial decision-making. A technological pipeline for visual data processing is proposed, comprising the stages of image normalization, segmentation, object localization, and classification, implemented with convolutional neural networks—specifically YOLO and U-Net architectures. An integrated product availability metric is introduced, which jointly accounts for physical, visual, and informational dimensions of availability. An optimization problem aimed at improving overall availability is formulated, and an adaptive neural network fine-tuning mechanism is implemented to enhance the accuracy of image recognition and segmentation, as well as the quality of analytical recommendations. Furthermore, an availability-improvement algorithm is proposed for a decision support system, based on the construction of an optimized merchandiser routing plan that prioritizes products and minimizes time expenditures. This routing problem is reduced to a generalized Traveling Salesman Problem (TSP) with priority-based weights. Methods for evaluating and enhancing product availability are proposed and described in detail. Based on the developed approaches and algorithms, a software system for monitoring and improving product availability has been implemented. Experimental results confirm the effectiveness of the proposed solutions: the average recognition accuracy reached 95.8%, and the integrated availability score achieved A = 0.93. The practical significance of this work lies in establishing an algorithmic foundation for intelligent shelf-monitoring systems that enable more efficient management of retail operations and inventory processes

• HARDWARRE-SOFTWARE FRAMEWORK FOR DEVELOPMENT OF MODULAR MOBILE ROBOTS WITH HIERARCHICAL ARCHITECTURE

  V.P. Andreev, V.L. Kim, S.R. Eprikov

  2020-07-10

  Abstract ▼

  In this paper, we consider the main problems associated with the need to integrate various robotic components into a single system while increasing the complexity of the navigation algo-rithms of mobile robots. The work aims to present a hierarchical modular architecture for reconfigurable mobile robots as a solution to the problems posed. In this architecture, a mobile robot is considered as a combination of modules, which in turn consist of simpler blocks - submodules. Each submodule includes a low-power microcontroller and is responsible only for the basic func-tions. A set of submodules forms a module - a transport platform, a robot leg, a manipulator, etc. Besides, one of the main objectives of the project is to provide a framework based on this architec-ture for rapid prototyping of robots from unified modules. The article describes the manufactured prototypes of the modules, briefly discusses the protocol of intermodular interaction of submodules connected by a CAN bus. The results of experiments on testing the protocol are presented and their analysis is given. The efficiency of the proposed solution limitations and a short plan of fur-ther actions for the implementation of the project are shown.