комп’ютерне бачення

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.

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.