# Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images
## 1 Introduction
In this work, our focus is on developing automated detection of AP landmines in thermal infrared images. Deep learning has shown remarkable success in various computer vision applications through continuous technological advancements in various aspects. Motivated by this, we organize appropriate techniques from the recent advancements of the computer vision community so as to devise a new method that can detect AP landmines effectively. The existing literature already includes several studies that utilize deep learning techniques for detecting AP landmines in visible and thermal infrared images. In [11], visible images of scatterable AP landmines obtained from an unmanned aerial vehicle are manually annotated. Then, the well-known object detector, Faster region-based convolutional neural network (Faster R-CNN) [15], is trained to detect the scatterable AP landmines. To improve the detection performance, pre-trained ResNet101 [16] is utilized. In [12], RGB and near-infrared images are firstly fused by a lightweight CNN, then Faster R-CNN detects scatterable AP landmines in the fused image. The fusion network and detector networks are simultaneously trained by minimizing both the low-level task (i.e., image reconstruction) and high-level task (i.e., landmine detection) losses. In [2], a mobile camera mounted on a demining robot captures visible images. After annotating the images, YOLOv8 [17] is trained to detect two types of surface AP landmines. In contrast to works that automatically detect AP landmines in visible images, most research on detecting AP landmines in thermal infrared images relies on classical human-designed detection methods [3, 4, 18, 19]. One notable exception is the study in [20], where a binary CNN classifier and the YOLOv8 object detector are explored. In the work, the binary classifier is trained to determine the presence of AP landmines in an input image, while YOLOv8 is trained to localize their positions. On the contrary to most previous studies that focus on localizing AP landmines in the input image, we propose a deep learning-based method that can output whether AP landmines exist in the input image. The motivation behind our choice of image-level detection is twofold. First, the dataset [14] used in this work provides only image-level labels. The study in [20] also utilizes the same dataset [14], where the authors manually annotated regions containing AP landmines and used these annotations to train an AP landmine detection model. However, in some cases, AP landmines are only partially visible due to vegetation, making it difficult to accurately annotate the regions containing them. This ambiguity can result in inconsistent labels, which may ultimately degrade the performance of the detection model [21]. The second reason is that if satisfactory performance can be achieved using only the presence information of AP landmines within an image, the cost and time required for annotating the collected images can be significantly reduced. The remainder of this paper is organized as follows. Section 2 briefly describes the dataset [14] used in this work and presents an analysis of it. Based on the insights gained from our examination, we present key solutions to the addressed problems in Section 3. Experimental results are reported in Section 4, and the conclusions are drawn in Section 5.