deep learning

Overview of deep learning and mobile edge computing in autonomous driving

In recent years, mobile edge computing and deep learning have attracted strong industry attention in the application scenario of autonomous driving. Mobile edge computing reduces the transmission delay of autonomous driving information by offloading computational tasks to edge servers to reduce the network load; deep learning can effectively improve the accuracy of obstacle detection, thereby enhancing the stability and safety of autonomous driving.

Analysis of framework networks for sign detection in deep learning models

This paper analyzes and compares modern deep learning models for the classification of MRI images of the knee joint. An analysis of modern deep computer vision architectures for feature extraction from MRI images is presented. This analysis was used to create applied architectures of machine learning models. These models are aimed at automating the process of diagnosing knee injuries in medical devices and systems.

Comprehensive Analysis of Few-shot Image Classification Method Using Triplet Loss

Image classification task is a very  important problem of a computer vision area. The first approaches to image classification tasks belong to a classic straightforward algorithm. Despite the successful applications of such algorithms a lot of image classification tasks had not been solved until machine learning approaches were involved in a computer vision area. An early successful result of machine learning applications helps researchers with extracted features classification which was not available without machine learning models.

Software Implementation of the Algorithm for Recognizing Protective Elements on The Face

The quarantine restrictions introduced during COVID-19 are necessary to minimize the spread of coronavirus disease. These measures include a fixed number of people in the room, social distance, wearing protective equipment. These restrictions are achieved by the work of technological control workers and the police. However, people are not ideal creatures, quite often the human factor makes its adjustments.

Analysis of Algorithms for Searching Objects in Images Using Convolutional Neural Network

The problem of finding objects in images using modern computer vision algorithms has been considered. The description of the main types of algorithms and methods for finding objects based on the use of convolutional neural networks has been given. A comparative analysis and modeling of neural network algorithms to solve the problem of finding objects in images has been conducted. The results of testing neural network models with different architectures on data sets VOC2012 and COCO have been presented.