РАЗРАБОТКА СИСТЕМЫ ОПРЕДЕЛЕНИЯ ПЕРЕМЕЩЕНИЯ РАБОЧИХ НА ПРОИЗВОДСТВЕ НА ОСНОВЕ OPENCV И АЛГОРИТМА ОДНОСТАДИЙНОГО ДЕТЕКТОРА

Abstract

It is a known fact that companies strive to cut their production costs. One of the most effective ways to reduce costs is to automate processes, for example, replacing employees with various robots or information systems. Often, at a construction site or a large-scale production, it is necessary to monitor employees' work. In its simplest form, monitoring can be carried out with the help of video cameras and people who view recordings from these cameras. However, this entails labor costs, taxes and other expenses. So, what one can do instead is introduce a system that tracks movements of workers, which will likely reduce the costs described above. To this day, machine learning and computer vision algorithms have developed quite strongly and allow the implementation of such tracking systems. There are already ready-made versions of tracking systems based on computer vision algorithms, but they are expensive, which makes them suitable only for large-scale industries. Another option is to develop your own tracking system, taking into account the nature of the business in which you plan to implement it. Aim. The authors aim at analyzing technologies that can be used to develop a system for tracking workers’ movements and proposing an implementation of such a system using machine learning algorithms and neural networks. Materials and methods. The authors give an overview of current solutions of monitoring systems based on RFID techno-logies, computer vision, GPS, and motion sensors, highlighting their pros and cons. The authors consider existing methods for motion detection based on computer vision and review known algorithms for detecting workers in special clothing. The YOLOv5 algorithm, consisting of backbone, neck and head, is considered in detail. Results. In the article, the authors provide a description of algorithms used for machine learning, computer vision, and neural networks. They propose an implementation of a system monitoring workers’ movements. They describe the modules that make up the tracking system and a diagram of the interaction of the modules, as well as the development of a motion detection algorithm and an algorithm for determining workers in special clothing.