# Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest
## 1 Introduction
According to [15], \"the neocognitron, introduced by [PERSON], is a self-organizing neural network that, after the self-organization process, acquires a hierarchical structure similar to the visual system model proposed by [PERSON] and [PERSON], allowing visual pattern recognition independent of its position\". This position-invariance feature is fundamental to developing modern CNNs, which use hierarchical layers to extract and recognize complex image features. [PERSON] et al. [16] introduced the backpropagation technique, which efficiently adjusts weights in CNNs by propagating the error backward through the layers and using the error gradient to minimize the difference between the network's actual output vector and the desired output vector. Each subsequent layer in a CNN combines features extracted from the previous layers, enabling the detection of increasingly complex features in images. [PERSON] et al. [17] contributed by applying the backpropagation algorithm to handwritten digit recognition, such as the MNIST dataset used for determining ZIP codes on mail, demonstrating CNNs' ability to handle distortions and positional variations. The findings of [18] further established that standard feedforward neural networks are universal approximators, capable of approximating any measurable function to the desired accuracy. This fact implies that shortcomings in applications can often be attributed to inadequate learning, insufficient hidden units, or stochastic relationships between input and target. In recent years, ref. [19] noted that \"machine learning techniques, especially when applied to neural networks, have played an increasingly important role in pattern recognition system design\". The advances in these techniques have enabled the creation of more accurate and efficient systems capable of handling a wide range of pattern recognition tasks across various fields. As noted by [20], CNNs have been widely applied in fields such as object detection, fault diagnosis, and image recognition. Since 2010, these applications have emerged as some of the most active research areas in computer vision. In the literature, several papers discuss target detection algorithms, specifically focusing on the YOLO model. Studies such as [11; 21], and more specifically [22; 23] develop research with the YOLOv3 model, while [24; 25] address YOLOv5. Ref. [26] provides an overview of YOLO models from YOLOv1 to YOLOv8, making them excellent resources for object detection studies. Additionally, works such as [27] address the limitations of traditional algorithms due to background noise and limited information, proposing improvements such as attention mechanisms in architecture to optimize the detection of small targets. Using such techniques to identify targets, ref. [28] performed target classification through transfer learning [29]. In their work, a CNN was used to extract image features. With these data, classification algorithms were used to determine whether the dataset contained airstrips. In his work, ref. [30] presented the first architecture of the so-called Visual Transformer (ViT), specializing in [10]'s work for computer vision. In the following years, several improvements were made, such as [31]'s Swin Transformer, which enhanced the architecture further. Later, ref. [32] presented the Global Context Visual Transformers (GCViT), which showed very promising results in image classification. The literature review reveals that studies on airstrip detection in the Amazon often address isolated aspects of the problem rather than providing a comprehensive solution. A notable exception is the work of [33], which employs various techniques to identify these targets. However, several areas in their proposed solution still require improvement. This study contributes by enhancing [33]'s algorithm by reducing the number of false positives while maintaining high levels of precision, addressing limitations in the original approach to better meet the specific challenges of this detection task.