Advancements in Landmine Detection DL-Based Analysis with Thermal Drones

Abstract

Landmines create a major humanitarian and ecological problem and dependable and scalable detection systems are required for safe removal operations. The traditional detection methods often suffer from increased false alarm rates, limited adaptability and expensive infrastructure, reducing their effectiveness across a range of landscapes. This study proposes a deep learning system for accurate identification and localization of landmines, using thermal drone imagery. A meticulously assembled collection of annotated thermal pictures was created, featuring classification labels and bounding box annotations for concurrent learning activities. Image normalization, scaling, feature extraction, and the organization of datasets for classification and detection processes were crucial to the preprocessing phase. For classification, various models such as VGG19, InceptionV3, ResNet50, MobileNetV3, Xception, and a Hybrid Ensemble of these models were used, while for detection and localization, models like YOLOv5, YOLOv8, and YOLOv11 were employed. An image upload, model inference, and presenting detection results through an interactive interface were achieved through a web framework built on Flask which allowed for real-time image uploads. The accuracy, precision, recall, F1-score, and mean average precision were used to assess the model. Experiments show the performance of ResNet50 and Hybrid Ensemble to be perfect with accuracy, precision, recall, and f1 score all reaching 100% while other detectors had a mean Average Precision of 0.755 and a precision of 0.730, respectively. The recommended architecture significantly enhances reliability and performance of landmine detection systems in automated landmine operations. “Keywords— Landmine detection, Drones, Accuracy, Imaging, Autonomous aerial vehicles, DL, Cameras, Thermal analysis, Thermal sensors, Surveys.”