title: Ð Ð°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð½Ð° ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ ÐºÐ°ÑÑÐ°Ñ ÑÐ°Ð´Ð°ÑÐ¾Ð² Ð½ÐµÐ¿ÑÐµÑÑÐ²Ð½Ð¾Ð³Ð¾ Ð´ÐµÐ¹ÑÑÐ²Ð¸Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: System for recognition and classification of objects for the radar map of continuous radars using convolutional neural networks
Creators: name: Ð£ Ð¡Ð¸Ð½ÑÑÐ¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð Ð°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð±ÑÐ°Ð·Ð¾Ð²
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
Ð½Ð°Ð±Ð¾Ñ Ð´Ð°Ð½Ð½ÑÑ
object classification
object detection
data set

description: ÐÐ±ÑÐµÐºÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ â Ð¡Ð¸ÑÑÐµÐ¼Ð° ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð´Ð»Ñ ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÐºÐ°ÑÑÑ ÑÐ°Ð´Ð°ÑÐ¾Ð² Ð½ÐµÐ¿ÑÐµÑÑÐ²Ð½Ð¾Ð³Ð¾ Ð´ÐµÐ¹ÑÑÐ²Ð¸Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ â 1. ÐÑÐ¸Ð¼ÐµÐ½Ð¸ÑÑ ÑÐ¿ÐµÑÐ¸Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ðµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾-Ð¼Ð°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ðµ "PyTorch machine learning library" Ð¸ "Shutil libraries" Ð½Ð° ÑÐ·ÑÐºÐµ Python Ð´Ð»Ñ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¹ Ð¸ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ñ Ð¿Ð¾ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð½Ð°Ð»Ð¸ÑÐ¸Ñ Ð°Ð²ÑÐ¾Ð¼Ð¾Ð±Ð¸Ð»ÐµÐ¹ Ð½Ð° ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ ÐºÐ°ÑÑÐ°Ñ Ð´Ð°Ð»ÑÐ½Ð¾ÑÑÑ-ÑÐ³Ð¾Ð» Ð² Ð¿Ð¾Ð»ÑÑÐ½ÑÑ ÐºÐ¾Ð¾ÑÐ´Ð¸Ð½Ð°ÑÐ°Ñ (SCA), Ð´Ð°Ð»ÑÐ½Ð¾ÑÑÑ-ÑÐ³Ð¾Ð» (DoA) Ð¸ Ð´Ð°Ð»ÑÐ½Ð¾ÑÑÑ-Ð°Ð·Ð¸Ð¼ÑÑ-Ð´Ð¾Ð¿Ð»ÐµÑ (RDA). 2. ÐÑÐµÐ´Ð»Ð¾Ð¶Ð¸ÑÑ Ð½Ð¾Ð²ÑÐµ Ð¸Ð´ÐµÐ¸ Ð¸ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ Ðº ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¾Ð±ÑÐµÐ´Ð¸Ð½ÑÐ½Ð½Ð¾Ð¹ ÑÐ²ÑÑÑÐ¾ÑÐ½Ð¾Ð¹ ÑÐµÑÐ¸ SCA+DoA+RDA Ñ ÑÑÐµÐ¼Ñ Ð²ÑÐ¾Ð´Ð°Ð¼Ð¸. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¸Ð·ÑÑÐµÐ½Ñ ÑÐµÐ¾ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð·Ð½Ð°Ð½Ð¸Ñ Ð¾ FMCW-ÑÐ°Ð´Ð°ÑÐ°Ñ Ð¸ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÑ Ð¿ÑÐ¸Ð½ÑÐ¸Ð¿Ð°Ñ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. ÐÐ¾ Ð´Ð°Ð½Ð½ÑÐ¼ ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð³Ð¾ Ð½Ð°Ð±Ð¾ÑÐ° SCORP Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð° ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ Ñ ÑÑÐµÐ¼Ñ Ð²ÑÐ¾Ð´Ð°Ð¼Ð¸. ÐÑÐ½Ð¾Ð²Ð½Ð°Ñ ÑÑÑÑÐºÑÑÑÐ° Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ ÑÐ¾ÑÑÐ¾Ð¸Ñ Ð¸Ð· ÑÐµÑÐµÐ´ÑÑÑÐ¸ÑÑÑ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ ÑÐ»Ð¾ÐµÐ² Ð¸ ÑÐ»Ð¾ÐµÐ² Ð¾Ð±ÑÐµÐ´Ð¸Ð½ÐµÐ½Ð¸Ñ. ÐÐ¾ÑÐ»Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ SCORP Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ Ð¼Ð¾Ð¶ÐµÑ ÑÑÐ´Ð¸ÑÑ Ð¾ Ð½Ð°Ð»Ð¸ÑÐ¸Ð¸ Ð°Ð²ÑÐ¾Ð¼Ð¾Ð±Ð¸Ð»Ñ Ð² ÑÐ°Ð´Ð¸ÑÑÐµ Ð´ÐµÐ¹ÑÑÐ²Ð¸Ñ ÑÐ°Ð´Ð°ÑÐ° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¾Ð´Ð½Ð¾Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð²Ð²Ð¾Ð´Ð° ÑÑÐµÑ ÑÐ°Ð´Ð¸Ð¾Ð»Ð¾ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹. ÐÐ¾Ð²Ð¸Ð·Ð½Ð° ÑÐ°Ð±Ð¾ÑÑ Ð·Ð°ÐºÐ»ÑÑÐ°ÐµÑÑÑ Ð² Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ Ð²ÑÑÑÐ½ÑÑ Ð½Ð°Ð±Ð¾ÑÐ¾Ð² Ð´Ð°Ð½Ð½ÑÑ Ð´Ð»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ ÑÑÐ¶Ð´ÐµÐ½Ð¸Ñ Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ ÑÑÐµÑÐ²ÑÐ¾Ð´Ð¾Ð²Ð¾Ð¹ ÑÑÑÑÐºÑÑÑÑ Ð´Ð»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÐµÐ³Ð¾ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸.
description: Object of study is system for recognition and classification of objects for the radar map of continuous radars using convolutional neural networks. The aim is 1. To apply the specialized software and mathematical software "PyTorch machine learning library" and "Shutil libraries" in Python to conduct research and solve problems on the development of convolutional neural networks to determine the presence of cars on radar maps range-angle in polar coordinates ( SCA), range-angle (DoA), and range-azimuth-doppler (RDA). 2. Offer new ideas and approaches to the development of a combined SCA + DoA + RDA convolutional network with three inputs. As a result of the study, theoretical knowledge about FMCW radars and the basic principles of convolutional neural networks were studied. Based on the data from the SCORP radar set, a convolutional neural network with three inputs was built. The basic structure of a neural network consists of alternating convolutional and pooling layers. After training the SCORP dataset, the neural network can judge the presence of a vehicle within the radar range based on the simultaneous input of three radar images. The novelty of the work lies in the use of manually classified data sets to improve the accuracy of judgment and the use of a three-input structure to improve its accuracy.