Efficient CNN Architecture for Multi-Modal Aerial View Object Classification
Abstract
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.
Cite
Text
Miron et al. "Efficient CNN Architecture for Multi-Modal Aerial View Object Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021. doi:10.1109/CVPRW53098.2021.00068Markdown
[Miron et al. "Efficient CNN Architecture for Multi-Modal Aerial View Object Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021.](https://mlanthology.org/cvprw/2021/miron2021cvprw-efficient/) doi:10.1109/CVPRW53098.2021.00068BibTeX
@inproceedings{miron2021cvprw-efficient,
title = {{Efficient CNN Architecture for Multi-Modal Aerial View Object Classification}},
author = {Miron, Casian and Pasarica, Alexandru and Timofte, Radu},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2021},
pages = {560-565},
doi = {10.1109/CVPRW53098.2021.00068},
url = {https://mlanthology.org/cvprw/2021/miron2021cvprw-efficient/}
}