computer vision

Efficiency and Accuracy: Comparison of Pir, Opencv With a Webcam, and Raspberry Pi

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.

SYSTEMIZATION OF REQUIREMENTS FOR OPERATIONAL QUALITY CONTROL SYSTEMS OF MEAT PRODUCTS

This paper presents a study on organizing requirements for automated meat quality control systems. It identifies key quality indicators–color, texture, marbling, and gloss–and analyzes the technical and functional parameters essential for practical assessment. The research highlights integrating computer vision, image processing, and machine learning algorithms to enhance objectivity, accuracy, and evaluation speed. The proposed approach aims to reduce human influence, enable real-time monitoring, and offer scalable solutions suitable for large-scale producers and small enterprises.

Exponential Data Augmentation Methods for Improving Yolo Performance in Computer Vision Tasks

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.

ADAPTIVE OBJECT RECOGNITION THROUGH A META-LEARNING APPROACH FOR DYNAMIC ENVIRONMENTS

Object recognition systems often struggle to maintain accuracy in dynamic environments due to challenges such as lighting variations, occlusions, and limited training data. Traditional convolutional neural networks (CNNs) require extensive labeled datasets and lack adaptability when exposed to new conditions. This study aims to develop an adaptive object recognition framework that enhances model generalization and rapid adaptation in changing environments.

DEVELOPMENT OF A PROGRAM FOR MODELING AND SIMULATING A COLLABORATIVE ROBOT WORKSPACE

The article presents the software development for modeling and simulating the workspace of a collaborative robot taking into account the presence of people. This is an important step in creating safe and efficient robotic systems within Industry 5.0 concept. The problem is posed by the need to ensure safety during the interaction of the robot with the operator, which is relevant for modern production processes with high human participation.

Advanced YOLO models for real-time detection of tomato leaf diseases

The increasing focus on smart agriculture in the last decade can be attributed to various factors, including the adverse effects of climate change, frequent extreme weather events, increasing population, the necessity for food security, and the scarcity of natural resources.  The government of Morocco adopts preventative measures to combat plant illnesses, specifically focusing on tomatoes.  Tomatoes are widely acknowledged as one of the most important vegetable crops, but they are highly vulnerable to several diseases that significantly decrease their productivity.  De

Implementation of presence detection with Haar cascade and local binary patterns histograms

School truancy is a significant problem that affects the educational environment and student achievement.  This article presents a project to develop an automated absence detection system for classrooms using Haar Cascade and Local Binary Patterns Histogram (LBHP) techniques.  The study begins by collecting a large dataset of classroom images, including various lighting scenarios and conditions.  Haar Cascade is used to detect human faces in images, followed by LBHP feature extraction for each detected face.  Experimental results demonstrate the effectiveness of the pro

Information System for Adapting Road Lane Segmentation Methods in Navigation Systems in Order to Increase the Accuracy of Road Signs Detection

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.

Development of an Algorithm and Software System for Facing Panels Accounting on Production Lines

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.