Efficient CNN Architecture for Multi-modal Aerial View Object Classification

被引：2

作者：

Miron, Casian ^{[1
]}

Pasarica, Alexandru ^{[1
]}

Timofte, Radu ^{[1
]}

机构：

[1] Gheorghe Asachi Tech Univ, MCC Resources SRL, Iasi, Romania

来源：

2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW 2021 | 2021年

关键词：

D O I：

10.1109/CVPRW53098.2021.00068

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The NTIRE 2021 workshop features a Multi-modal Aerial View Object Classification Challenge. Its focus is on multi-sensor imagery classification in order to improve the performance of automatic target recognition (ATR) systems. In this paper we describe our entry in this challenge, a method focused on efficiency and low computational time, while maintaining a high level of accuracy. The method is a convolutional neural network with 11 convolutions, 1 max pooling layers and 3 residual blocks which has a total of 373.130 parameters. The method ranks 3rd in the Track 2 (SAR+EO) of the challenge.

引用

页码：560 / 565

页数：6

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