система технічного зору

In today’s world of globalized trade and e­commerce, product labeling is becoming increasingly important. It ensures product traceability throughout the supply chain, provides information and protection, and influences consumer confidence. Traditional methods of checking and reading labels are based on manual control or the use of simple barcode scanners, which often prove ineffective in conditions of increasing data processing volumes.

The goal of the article is to provide a methodology of improving images quality in low-light conditions trough fusion of data received from telecamera and thermal camera. Data from thermal camera uses for compensation of significant illumination reduction in poor lighting conditions and allow keep required level of information. Proposed method establishes dynamic regulation of fusion coefficients depending on brightness level to minimize artifacts, increase edge sharpness, and improve object detectability.

This paper is dedicated to developing and evaluating the facial recognition system, focusing on its effectiveness and operational reliability under real-world conditions. The choice of the Raspberry Pi hardware platform for implementing the system has been justified by its capability to process video streams in real time, as well as its compatibility with the high-quality Raspberry Pi Camera V2, which enables the acquisition of images with sufficient resolution for the proper functioning of computer vision algorithms.

The article examines data augmentation methods in the task of image recognition, specifically introducing the exponential augmentation approach to enhance the performance of deep neural networks, particularly YOLO, in object detection tasks. The proposed methodology is based on the sequential and repeated application of various transformations, including horizontal and vertical flipping, 90° rotation, Gaussian Blur, brightness and contrast adjustment.

In today’s world, where the speed of technological change is extremely impressive, the traffic industry is not left behind. The use of lane segmentation on the road is becoming a key element not only for safety, but also for improving navigation and traffic sign detection systems. This approach opens the door to a new level of efficiency and accuracy in traffic management, helping to improve the quality and safety of our movement. Let’s dive into the details of this exciting and promising area of road transport technology development.

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.