Improving the accuracy of landmine detection using data augmentation: a comprehensive study

Abstract

In areas such as landmine detection, where obtaining large volumes of labeled data is challenging, data augmentation stands out as a key method. This paper investigates the role and impact of different data augmentation methods, and evaluates their effectiveness in improving the performance of deep learning models adapted to landmine detection. Landmine detection is governed by international security requirements on the one hand, and urgent humanitarian needs on the other. This field, characterized by its urgency and the requirement for meticulous accuracy, is key against the explosive ordnance. The hidden dangers of these munitions go beyond direct physical damage, leaving their mark on the socio-economic structures of the affected regions. They hinder agricultural activities, impede the restoration of infrastructure and create obstacles to the return and resettlement of displaced populations. The mission to detect and neutralize these hidden hazards combines advanced technology with an unwavering commitment to humanitarian principles to leave future generations with a land cleared of the heavy legacy of past wars. The effectiveness of machine learning models in detecting landmines is inextricably linked to the diversity, volume and reliability of the data they are trained on. The effort to collect a diverse and representative dataset is fraught with challenges, given limitations related to accessibility, ethical considerations and security issues. The lack of comprehensive data poses significant obstacles to the development and refinement of machine learning algorithms, potentially limiting their ability to operate effectively in diverse and unpredictable areas. In response to these limitations, data augmentation has become an important method. It is a way to circumvent data limitations by supplementing existing datasets with synthesized variations. Augmentation strategies include spatial alignment, pixel intensity manipulation, geometric transformations, and compositing, each of which is designed to give the dataset a semblance of real-world variability. This study explores the various applications of data augmentation in the field of landmine detection. It emphasizes the importance of augmentation as a means of overcoming data limitations.