Landmine Detection Based on Faster R-CNN in UAV Platform

Landmine detection is not just a technical task, it is also a crucial element in alleviating humanitarian disasters and aiding in the reconstruction of communities living under the threat of landmines. This undertaking not only saves lives but also offers people the opportunity to rebuild stable lives. The integration of drones into this context significantly enhances coverage and safety measures, providing a substantial improvement for thorough and comprehensive landmine target detection with valuable multi-modal data sources. This paper revolves around the pivotal task of capturing and collecting landmine datasets, utilizing images obtained through unmanned aerial vehicles (UAVs). The model, based on the trained faster region-based convolutional neural network (Faster R-CNN) algorithm, is intended to serve as a valuable tool for subsequent activities such as landmine monitoring, identification, and in-depth analysis, thereby offering reliable recommendations for enhanced decision-making.