This thesis presents a comprehensive investigation into machine learning approaches forlandmine detection using thermal imagery. It addresses both classification and precise lo- calization challenges that are integral for humanitarian demining operations. The research encompasses two complementary methodological frameworks: comparative evaluation of traditional machine learning versus deep learning approaches, then followed by an imple- mentation of hyperparameter optimization for enhanced safety performance. The foundation of the study demonstrates that traditional machine learning methods achieve competitive classification performance. Conventional models achieved significant performance with the Random Forest and RestNet50 respectively scoring accuracies of 91.88% and 94.29%, though struggle to achieve >10% when tasked with classification and localization. Expanding on this foundation, we addresses this gap through multi-task learn- ing frameworks that simultaneously optimize for both detection and precise localization. Through systematic hyperparameter tuning across 64 configurations, the optimized multi- task approach achieves 90% detection accuracy with 92% precision while providing precise bounding box localization, representing a 37.5% reduction in false negatives. These find- ings demonstrate that while traditional machine learning offers computational efficiency for basic detection, multi-task deep learning frameworks provide significant performance gains when requiring precise spatial localization, which is an important requirement in demining operations.
Author Keywords: computer-vision, demining, humanitarian, Landmines, Multi-Task Learning, YOLO