Prospects of using deep learning models for semantic image segmentation on autonomous devices

Abstract

Relevance. The implementation of deep learning models for semantic segmentation on autonomous devices is a promising direction for the development of intelligent systems capable of analyzing visual information without constant connection to external resources. This enables the creation of more autonomous and efficient systems that can operate in real-time and under resource constraints. Such an approach is highly significant for various industries, including robotics, autonomous vehicles, medical diagnostics, and other fields where high accuracy and speed of image processing are required.

Goal. The goal of this work is to explore the possibilities and challenges of using deep learning models for semantic segmentation on autonomous devices. This includes analyzing the efficiency of the models, their adaptation to the limited resources of the devices, and developing methods to ensure the security of access to the trained models.

Research methods. The research methods include theoretical analysis, systematization, and generalization of the use of deep learning models in autonomous devices. Special attention is given to the parameters affecting the memory footprint of the models and the specifics of implementing trained models into proprietary software products. Additionally, modern approaches to encrypting models to ensure their security have been considered.

Results. A comparative analysis of traditional models and deep learning models for semantic segmentation of images has been conducted. Significant potential of deep learning technology for creating autonomous intelligent systems is identified. Various deep learning models currently used for semantic segmentation of images have been reviewed. The impact of key parameters on the efficiency of models on devices with limited resources has been determined, and the role of model size has been considered. Recommendations for implementing trained models into software products are presented, including optimizing models to reduce the size and increase the speed. Special attention has been paid to the analysis of encrypting trained models. It is shown that ensuring the security of access to trained deep learning models for semantic segmentation of images on autonomous devices requires a comprehensive approach that combines hardware and software solutions.

Conclusions. Further developments in the field of deep learning for semantic segmentation on autonomous devices will contribute to the development of more efficient and autonomous systems for a wide range of applications, including computer vision, robotics, and more. Ensuring the security of access to trained deep learning models for semantic segmentation of images on autonomous devices requires a comprehensive approach that combines hardware and software solutions. This not only protects the intellectual property of developers but also ensures the integrity and confidentiality of the data processed by autonomous devices when performing semantic segmentation tasks.