Remote Sensing of Artisanal Mines Buried in the Ground by Infrared Thermography Using UAV

Publication Type

Conference Proceeding

Publication Date (Issue Year)

2024

Journal Name

Springer Proceedings in Mathematics of Computer Science, Cybersecurity and Artificial Intelligence

Abstract

The antipersonnel and anti-tank landmines create a lot of human and material damage in the Sahel countries affected by terrorism. Explosive mine detection methods are based on tools handled by human operators and target industrial metal mines. These methods are risky and limited because the types of mines most commonly used in the Sahelian context are mainly homemade and are encased in various local materials such as metal, plastic, glass, ceramic, or wood. This chapter presents a solution for remote sensing of artisanal mines buried in the ground using infrared thermography. A DJI Phantom 4 Quadcopter equipped with a FLIR thermal camera and a GNSS sensor performs an automatic low-level flyover of the potentially mined road. Thermal images of the road are collected with an overlap rate of 80% and referenced with the GNSS sensor. Photogrammetry algorithms are used to process the thermal images to detect and locate anomalies related to the presence of buried mines. Despite the limitations due to environmental influences, the model showed a detection rate of 75% during flights at an altitude of 6 m and a speed of 3 m/s. The experimental results show a good correlation between the thermal contrast of the mathematical model and the cooler areas containing a mine-related chemical substance.

Keywords

Remote Sensing, Artisanal Mines

Rsif Scholar Name

Adama Coulibaly

Rsif Scholar Nationality

Burkina Faso

Cohort

Cohort 4

Thematic Area

ICTs Including Big Data and Artificial Intelligence

Africa Host University (AHU)

University of Gaston Berger (UGB), Senegal

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