Abstract
This article presents a dataset of thermographic images of terrain with antipersonnel mines to identify the presence or absence of these artifacts using machine learning and artificial vision techniques. The dataset has 2700 thermographic images acquired at different heights, using a Zenmuse XT infrared camera (7-13 µm), embedded in the DJI Matrice 100 drone. The data acquisition experiment consists of capturing aerial infrared images of a terrain where elements with characteristics similar to antipersonnel mines type legbreaker were buried. The mines were planted in the ground between 0 cm and 10 cm deep and were spread over an area of 10 m x 10 m. The drone used a flight protocol that set the trajectory, the time of the flight, the acquisition height, and the image sampling frequency. This dataset was used in "Detection of "legbreaker" antipersonnel landmines by analysis of aerial thermographic images of the soil" [7].