Tiny-Inception-ResNet-V2: Using Deep Learning for Eliminating Bonded Labors of Brick Kilns in South Asia
Abstract
This paper proposes to employ a Inception-ResNet inspired deep learning architecture called Tiny-Inception-ResNet-v2 to eliminate bonded labor by identifying brick kilns within "Brick-Kiln-Belt" of South Asia. The framework is developed by training a network on the satellite imagery consisting of 11 different classes of South Asian region. The dataset developed during the process includes the geo-referenced images of brick kilns, houses, roads, tennis courts, farms, black farms, dense trees, orchards, parking lots, parks and barren lands. The dataset is made publicly available for further research. Our proposed network architecture with very fewer learning parameters outperforms all state-of-the-art architectures employed for recognition of brick kilns. Our proposed solution would enable regional monitoring and evaluation mechanisms for the Sustainable Development Goals.
Cite
Text
Nazir et al. "Tiny-Inception-ResNet-V2: Using Deep Learning for Eliminating Bonded Labors of Brick Kilns in South Asia." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.Markdown
[Nazir et al. "Tiny-Inception-ResNet-V2: Using Deep Learning for Eliminating Bonded Labors of Brick Kilns in South Asia." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/nazir2019cvprw-tinyinceptionresnetv2/)BibTeX
@inproceedings{nazir2019cvprw-tinyinceptionresnetv2,
title = {{Tiny-Inception-ResNet-V2: Using Deep Learning for Eliminating Bonded Labors of Brick Kilns in South Asia}},
author = {Nazir, Usman and Khurshid, Numan and Bhimra, Muhammad Ahmed and Taj, Murtaza},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2019},
pages = {39-43},
url = {https://mlanthology.org/cvprw/2019/nazir2019cvprw-tinyinceptionresnetv2/}
}