# Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images
## 1 Introduction  
Automatic detection of anti-personnel (AP) landmines is crucial to enhance safety, efficiency, and accuracy in demining operations [1]. Traditional manual detection methods expose personnel to significant risks and are often slow and labor-intensive. Automated systems, such as robotic vehicles [2] and drones [3], equipped with various sensors, can navigate hazardous areas without endangering human lives, thus accelerating the clearance process and reducing casualties. In addition, the automatic system working in real time can provide necessary information to detect AP landmines at the operation site instantly. These technological advances not only improve the speed and accuracy of mine detection, but also facilitate the safe reclamation of lands contaminated by AP landmines, ultimately contributing to the protection of civilians and the restoration of affected communities [4]. Various sensing and imaging equipments have been employed to detect landmines, such as traditional metal detectors, instruments that can measure the magnetic polarizability tensor of a conducting object [5], ground-penetrating radar [6, 7], and optical imaging sensors [2, 3]. AP landmines can be classified into two types depending on the type of constituent materials: metal AP landmines and low-metal-content AP landmines [4]. APlandmines commonly encountered in practice are plastic types [3; 4]. Therefore, sensors utilizing the principle of electromagnetic field are not suitable for practical AP landmines. On the other hand, ground-penetrating radar shows promising performance to detect both metallic and non-metallic objects. Optical imaging sensors used to detect AP landmines can be further divided into the following: hyperspectral imaging, visible imaging, and thermal infrared imaging sensors. Hyperspectral imaging sensors collect data in hundreds of narrow and contiguous spectral bands, unlike standard RGB cameras that capture only three color bands (red, green, blue). This characteristic can help distinguish AP landmines from surrounding materials (e.g., soil and vegetation) by analyzing spectral differences [8] or comparing the spectral signatures of AP landmines in the library [9]. However, simply considering spectral anomalies as AP landmines in the hyperspectral image can increase the number of false alarms. In addition, preprocessing is required to mitigate environmental effects in the detection of spectral signatures [10]. Visible imaging sensors are mainly used to detect surface AP landmines [2; 11; 12]. The representative of surface landmines is the scatterable landmine characterized by its small size, high composition of plastic, and easy deployment by aerial or armored vehicles [11; 12]. As the resolution of visible images is generally higher than that of other optical images and the shape of scatterable landmines is prominent compared to other natural objects, the scatterable landmine can be detected in the visible image. However, the detection may fail when the scatterable landmines are heavily occluded by ground vegetation or obscured by the shadow of surrounding objects. AP landmines and the soil in which they are buried exhibit distinct thermal properties, primarily due to differences in thermal conductivity, heat capacity, and thermal diffusivity [13]. These differences influence how heat is transferred and retained, leading to detectable thermal contrasts on the soil surface [4] crucial for detection methods that utilize thermal infrared imaging sensors. As environmental conditions such as ambient temperature, solar radiation, relative humidity, and wind speed can influence the thermal contrast [14], these factors not only should be considered in the thermal image acquisition but may also limit the use of the thermal imaging sensor in AP landmine detection. Overall, rather than solely being used, different sensing and imaging equipments can collectively contribute to configuring more reliable automated mine detection systems, improving operational effectiveness.