This article examines the peculiarities of warehouse management systems and presents the principles and implementation of an in-house software system for warehouse management using computer vision technology.
This paper aims to develop and implement an algorithm and an automated software system for the auto- matic accounting process of external facing panels during transportation on line conveyors. The method described in this paper is designed to simplify the process of production and accounting of wall-facing panels. This method can also serve as a model for implementing other manufacturers. The developed algorithm consists of the following steps: obtaining a video stream in real-time or from a file and its targeted processing and determining the number of moving objects of interest.
This article explores the development of a video surveillance system that utilizes cuttingedge technology to analyze the video stream in real-time, identify motion, and recognize objects within the video stream. The functionality of this system enables it to provide a high level of accuracy in identifying objects, even in low-light conditions or with low-resolution cameras. The software system has been designed as a user-friendly desktop application with the latest technologies and features that will ensure its relevance and easy maintenance in the future.
This paper analyzes and compares modern deep learning models for the classification of MRI images of the knee joint. An analysis of modern deep computer vision architectures for feature extraction from MRI images is presented. This analysis was used to create applied architectures of machine learning models. These models are aimed at automating the process of diagnosing knee injuries in medical devices and systems.
In many cases, visual and optical methods can be used in defectoscopy for different materials and products. With the development of microprocessor components and significant expansion of usage of computer technologies and image processing and analysis techniques in different areas, the use of visual and optical methods in defectoscopy for production and research purposes is rapidly developing.
Image classification task is a very important problem of a computer vision area. The first approaches to image classification tasks belong to a classic straightforward algorithm. Despite the successful applications of such algorithms a lot of image classification tasks had not been solved until machine learning approaches were involved in a computer vision area. An early successful result of machine learning applications helps researchers with extracted features classification which was not available without machine learning models.
The quarantine restrictions introduced during COVID-19 are necessary to minimize the spread of coronavirus disease. These measures include a fixed number of people in the room, social distance, wearing protective equipment. These restrictions are achieved by the work of technological control workers and the police. However, people are not ideal creatures, quite often the human factor makes its adjustments.
The aim of the work is to develop an automated measuring system in a mechanical gyrocompass with the help of specially developed hardware and software in order to facilitate the operation of the device and minimize observer errors. The developed complex provides automation only for the time method, as for the method of the turning point it is necessary to constantly contact the motion screw of the total station. The project is based on an integrated system, the hardware part of which contains a single-board computer, camera, and lens.
This paper examines the main methods and principles of image formation, display of the sign language recognition algorithm using computer vision to improve communication between people with hearing and speech impairments. This algorithm allows to effectively recognize gestures and display information in the form of labels. A system that includes the main modules for implementing this algorithm has been designed.
Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed.