IMAGE SEGMENTATION BASED ON ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS IN INDUSTRIAL AND SECURITY SYSTEMS

This article analyzes artificial intelligence-based image segmentation methods, their theoretical foundations, and practical applications, with particular emphasis on the capabilities of modern deep learning algorithms—convolutional neural networks (CNN), U-Net, and Mask R-CNN—in achieving high-precision segmentation results. It also highlights the importance of these technologies in industrial applications such as automation of production processes, defect detection, and quality control improvement, as well as in security systems for object detection, enhancing surveillance efficiency, and early identification of hazardous situations, while also discussing real-time performance efficiency, computational complexity, and prospects for integration into practical systems.