An improved mining image segmentation with K-Means and morphology using drone dataset - Haqiq, Nasreddine; Zaim, Mounia; Sbihi, Mohamed; El Alaoui, Mustapha; Masmoudi, Lhoussaine; Echarrafi, Hamza (International Journal of Electrical and Computer Engineering (IJECE), 2024)
The mining industry faces the challenge of incorporating advanced technology to explore new ways of increasing productivity and reducing costs. Our focus is on integrating drone technology to revolutionize mining tasks like inspection, mapping, and surveying. Drones offer a precision advantage over traditional satellite methods. To this end, we have created a dataset consisting of 373 aerial images captured by a DJI Phantom 4 drone, which depict a mining site in the Benslimane region of Western Morocco. These images, with a ground resolution of 2.5 cm per pixel, are the basis of our research. Our study aims to address the challenges posed by traditional mining techniques and to leverage technological innovations to improve segmentation and classification. The proposed approach includes new methodologies, particularly the combination of K-Means clustering and mathematical morphology, to overcome limitations and deliver better segmentation results. Our findings represent a significant step forward in advancing mining operations through the effective use of modern technologies.