Semantic Image Segmentation on Autonomic Devices: Prospects for Implementing Deep Learning Models

The implementation of deep learning models for semantic segmentation on autonomic devices is a promising direction in developing intelligent systems capable of analyzing visual information independently. The present study explores the possibilities and challenges of using such models, employing theoretical analysis, systematization, and generalization of their usage in autonomic devices. Our research reveals significant potential for this technology, identifying key parameters influencing model efficiency on resource-constrained devices and proposing implementation recommendations. Furthermore, we highlight contemporary approaches to model encryption, emphasizing the need for a comprehensive security approach combining hardware and software solutions. The study concludes that advancements in this field will contribute to more efficient autonomic systems across various applications, including computer vision and robotics. By addressing technical challenges and security considerations, this research paves the way for robust autonomic visual processing systems, potentially revolutionizing industries reliant on intelligent image analysis. The relevance of this study lies in its practical insights into implementing deep...