# Improved Ship Detection Algorithm from Satellite Images Using YOLOv7 and Graph Neural Network
## 1 Introduction  
The management of marine security relies significantly on remote sensing images for the automatic ship detection. Its primary duties include keeping an eye on traffic, finding illicit fishing, and stopping maritime pollution. Military organizations use the automatic ship detection system to enhance maritime security. This process can be carried out through various activities, such as reconnaissance, surveillance, and intelligence. One of the most common technologies used in this field is advanced remote sensing. This type of technology is used to gather various types of data. It can gather various data points such as radar, electro-optical cameras, and electronic support systems. This research focuses on analyzing satellite photos. Deep learning is a process that requires a lot of training data to develop. All commercial and passenger ships weighing over 300 tons are required to have an automatic identification system (AIS) transponder. This type of device transmits information about the vessel's location and destination. However, it can be easily manipulated. For instance, if a fishing boat wants to pretend to be another vessel, it can alter the type of information that the ship transmits. Convolutional neural networks (CNNs), a tiny subset of machine learning (ML), are among the more recent technologies that have seen more successful implementations [1]. Additionally, they have been integrated with a multi-layered network architecture created using conventional neural network techniques. CNNs consist of various components, such as activation function, input layers, output layers, and convolutional layers. Ship identification and classification have also been accomplished using a deep learning strategy [2], a process of deep learning influenced by the human brain's structure and function. It can be used to process the data collected by the SAR system, which include monitoring plants and diseases, mapping various trajectories, and analyzing the data collected from various sources. Therefore, the primary goal of this study is to identify the presence of ships using satellite photos with high accuracy. In this study, we suggest going a step further in addressing the issue in the automatic identification of ships. When compared to handcrafted features, our method, which is based on the well-known CNN architecture You Only Look Once (YOLO), can determine the most distinctive features for the given task [3]. In the suggested framework, picture features are extracted through Graph Neural Networks (GNNs) and then categorized using a YOLOv7 detector. The HRSID dataset was used to train the automatic ship detection system; a comparison was made with various CNNs (YOLOv3, YOLOv4, YOLOv5 and YOLOv6) presented by other authors [4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. We evaluated our approach using a publicly accessible ship dataset made up of around 16 K satellite photos that also included moving ships. The summary of the contribution is as follows:  
1. For ship detection, a high-resolution SAR dataset is used. It was not able to take into account the various flaws in the previous SAR ship dataset, which is mainly used for CNN-based detectors. 2. The goal of this paper is to analyze the effects of ship detection on the images captured by the SAR system. A large-sized image of the ship is used to test the model's performance. 3. A comprehensive evaluation of ship detection is performed using MS COCO metrics. The IoU threshold of objects is evaluated using an average precision. An HRSID comparison between different YOLO versions is also carried out. The organization of paper is as follows. The related work and state of the art on ship detection from satellite images, including those that employ DL algorithms for classification, is briefly summarized in Section 2. Then, in Section 3, we provide our DL-based YOLOv7_GNN ship detection approach. The examined datasets and a thorough examination of the stated outcomes resulting from the executed experiments are both provided in Section 4.