Brick Kiln Detection and Localization using Deep Learning Techniques

Brick kiln areas being home to practices of forced labor and environmental pollution are difficult to detect. This paper explores the automatic detection and localization of brick kilns in satellite images by the implementation of Deep Learning Techniques to aid the government to take action against forced labour and environmental pollution. An artificially intelligent model was trained to automatically detect and create a bounding box around the kilns in satellite images from the South Asian brick belt. The experimentation started with the use of basic convolutional neural networks to identify kilns in an image and then progressed to the more advanced You Only Look Once (YOLOv3) algorithm to draw bounding boxes across the kilns. Our research enabled us to successfully build a convolutional neural network for the identification of kilns which returned an accuracy of 97.27%. For the localization of kilns using the bounding box approach, our research enabled us to implement the YOLOv3 algorithm which returned an average loss of 0.13 and an average IOU of 0.75. The work done in this project has been promising thus far and can be further enhanced to build a Graphical User Interface to help governments to take action against the social issues of bonded labor and environmental issues that are prevalent within brick kilns.