Detection and Classification of Vehicles in Ultra-High Resolution Images Using Neural Networks

被引：0

作者：

Chen, Ch ^{[1
,2
]}

Minald, A. A. ^{[3
]}

Bohush, R. P. ^{[4
]}

Ma, G. ^{[5
]}

Weichen, Y. ^{[5
]}

Ablameyko, S., V ^{[3
,6
]}

机构：

[1] Zhejiang Shuren Univ, Sch Informat Sci & Technol, Hangzhou, Peoples R China

[2] Int Sci & Technol Cooperat Base Zhejiang Prov Rem, Hangzhou, Peoples R China

[3] Belarusian State Univ, Minsk, BELARUS

[4] Polotsk State Univ, Novopolotsk, BELARUS

[5] EarthView Image Inc, Huzhou, Peoples R China

[6] Natl Acad Sci Belarus, United Inst Informat Problems, Minsk, BELARUS

来源：

JOURNAL OF APPLIED SPECTROSCOPY | 2022年 / 89卷 / 02期

关键词：

detection and classification of objects; images of the earth's surface; ultrahigh resolution images; neural network;

D O I：

10.1007/s10812-022-01361-1

中图分类号：

O433 [光谱学];

学科分类号：

0703 ; 070302 ;

摘要：

A deep neural network architecture is proposed based on integrating the convolutional neural network Faster R-CNN with the Feature Pyramid Network module. Based on this approach, an algorithm is developed for detecting and classifying transport media in images along with a corresponding model. The cross-platform medium ML.NET is used to teach the proposed model. Results of comparing the effectiveness of the application of the proposed approach and the convolutional neural networks YOLO v4 and Faster R-CNN are presented. An improved accuracy of detection and localization of different types of vehicles in ultra-high resolution images is demonstrated. Examples of the processing of images of the earth's surface in ultra-high resolution are given, along with corresponding recommendations.

引用

页码：322 / 329

页数：8

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