# Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images
## 2 Dataset  
### AP Landmine Dataset  
A publicly available dataset for the detection of \"legbreaker\" AP landmines [3, 14] is selected in this work. Although another dataset exists for AP landmine detection experiments [4], it was constructed using homogeneous terrain, which may cause experimental results obtained from it to differ significantly from those encountered in real-world field inspections [3]. To construct the dataset, authors of [3] produced eight replicas of AP landmines used by armed groups in Colombia. The replica measures 8.7 cm in diameter and 10.2 cm in height. The replicas were installed on a square soil area of 10 m side approximately with scarce vegetation [3]. The area was divided into nine zones, with AP landmine replicas buried at depths of 0, 1, 5, and 10 cm in eight of them. The remaining zone was left free of AP landmines. Thermographic images of the nine zones were captured using a DJI Zenmuse XT camera (DJI, Shenzhen, China), operating in the spectral range of 7.5 to 13.5 \(\upmu\)m, mounted on a DJI Matrice 100 unmanned aerial vehicle (DJI) (UAV) [3]. For each zone, the altitude of the UAV was gradually decreased from 10 to 1 m in one meter decrements to capture images of AP landmines at varying apparent sizes. Some example images of the dataset are given in Figure 1.  
Images of the dataset are provided in three formats: JPG, TIFF, and R-JPG. The R-JPG format was selected for our analysis and experiments as its image resolution (336 \(\times\) 256) matches the pixel count of the infrared camera specified in [3]. The following metadata is given along with each image: date of image acquisition, flight altitude of the UAV at the time of image acquisition, central temperature of the image, and the zone number indicating the presence and depth of AP landmines. Therefore, we label images as positive if the zone number belongs to one of the eight zones where AP landmines were buried, and as negative otherwise. The images in the dataset are utilized without any preprocessing, with a single exception. When the UAV operates at a high altitude over a landmine-free zone, the infrared camera captures portions of adjacent APlandmine-contaminated areas in addition to the intended landmine-free region. To prevent discrepancies between the label information and the corresponding input image, only the landmine-free regions are cropped from the mixed images. Accordingly, we thoroughly examined the negative images to identify any mixed captures and manually cropped the landmine-free regions from all such images. Figure 2 shows examples of the mixed image and the cropped landmine-free image. The dataset comprises 659 images, including 68 landmine-free (negative) images and 591 AP landmine (positive) images. This reflects (1) a significant class imbalance that may adversely affect the training process and (2) a limited overall dataset size, which results in variations in the performance evaluation of the model depending on the train-test split. A simple and widely used approach to address class imbalance is oversampling, which involves increasing the number of underrepresented class samples using techniques such as sample duplication [22]. Therefore, we simply duplicate the negative samples so as to match the number of negative samples with that of the positive samples in the training phase only. Before duplicating the negative samples, we split the dataset into the training and test sets by preserving the ratio of the negative and positive samples in the original dataset. Figure 1: Example images of areas with AP landmines (**top** row) and without AP landmines (**bottom** row). In the top row, AP landmines are buried at depths of 0, 1, 5, and 10 cm from left to right. Red boxes surrounding AP landmines are not given in the dataset and depicted here only for visualization purposes. Figure 2: Examples of mixed images (**top** row) and the corresponding cropped landmine-free regions (**bottom** row). An AP landmine is observed to the right of the landmine-free region in the top row.