Tiny-Inception-ResNet-v2: Using Deep Learning for Eliminating Bonded Labors of Brick Kilns in South Asia
This work addresses bonded labor monitoring in South Asia, offering a domain-specific tool for regional evaluation of Sustainable Development Goals, though it is incremental as it adapts existing architectures.
The paper tackles the problem of identifying brick kilns in South Asia to combat bonded labor by proposing Tiny-Inception-ResNet-v2, a deep learning architecture trained on satellite imagery, which outperforms state-of-the-art methods for brick kiln recognition.
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, sparse trees, 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.