# Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images
## 3 Methods  
### Transfer Learning  
Existing studies have shown that increasing both the diversity and quantity of training samples together with the model capacity can improve the performance of trained models [30]. For applications like ours, where the cost and effort to collect a sufficient number of training samples are unaffordable, this approach is not feasible. By leveraging the transfer learning technique, the pre-training, fine-tuning, and prediction paradigm [31], the problem of the small-sized dataset can be overcome. In this approach, a model is initially pre-trained on a large-scale dataset to learn general representations and is subsequently fine-tuned on a smaller, task-specific dataset to adapt to the target application. Depending on the availability of labels in the pre-training dataset, pre-training can be categorized as either supervised or self-supervised. The representative example of a supervised pre-training model is a CNN trained on the ImageNet [25] dataset with a supervised loss. However, supervised pre-training relies on large-scale labeled data for pre-training, which can limit the scalability of the dataset and hinder the learning of more powerful features. The recent advances in self-supervised learning [32; 33] show that useful and transferable representations can be learned without labels. As self-supervised pre-training can cope well with the scalability of training sample sizes, it even shows better performance in downstream tasks compared to supervised pre-training [34]. Advantages of utilizing pre-trained models for downstream tasks can be summarized as follows. First, pre-trained models can be applied across a wide range of downstream tasks, making them highly versatile. Second, they enable significantly improved performance using only a small amount of annotated data, compared to training models from scratch. Most importantly for our work, even in the presence of a substantial domain gap between the pre-training and fine-tuning datasets, pre-trained models can still enhance downstream task performance through adaptation. Notable examples include land cover classification from remotely sensed images [35] and fault classification of solar modules using infrared imagery [36], both of which have successfully employed ImageNet pre-trained models. These advantages naturally lead us to leverage pre-trained models to cope with a limited number of the AP landmine dataset. In this work, a total of five pre-trained models are explored. A summary of models employed in this work is given in Table 1. The first  
Figure 3: Example images of the three classes in MASATI dataset. Images of “Coast” and “Sea” classes do not contain ships while those of “Coast & Ship” do. pre-trained model is the residual network (ResNet) [16] learned on the ImageNet1K (IN1K) dataset [25]. The distinct characteristic of ResNet compared to previous CNN models is the residual block architecture, which incorporates identity shortcut connections that bypass one or more layers. Instead of directly learning a desired underlying mapping \(H(x)\), residual blocks are designed to learn a residual function \(F(x)=H(x)-x\). This architecture facilitates the flow of gradients during backpropagation, effectively addressing the vanishing gradient problem and enabling the successful training of networks with substantially increased depth [16]. Due to its robustness and ease of optimization, ResNet models have been utilized in various domain-specific applications such as remote sensing [35] and medical image analysis [37]. The second model we investigate is ConvNeXt [38] pre-trained on the ImageNet21K (IN21K) [25], a modernized CNN inspired by Vision Transformers (ViTs). ConvNeXt is designed to narrow the performance gap between CNN models and ViT models while preserving the efficiency and inductive biases inherent to convolutions [38]. ConvNeXt incorporates several architectural refinements borrowed from ViT [42].