# YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
## 1 Introduction  
Object detection serves as a prerequisite for advanced visual tasks such as scene understanding. Compared to object detection in videos, detecting objects in static images is more challenging. The utilization of object detection in optical remote sensing images has been widespread, encompassing diverse applications such as monitoring, traffic surveillance, agricultural development, disaster planning, and geospatial referencing [1; 2; 3; 4]. This has attracted considerable interest from researchers in recent years. Traditional remote sensing image object-detection algorithms can be categorized into several types: threshold-based methods, feature engineering-based methods, template matching methods, machine learning-based methods, segmentation-based methods, and spectral information-based methods [5]. (1) Threshold-Based Methods: These methods typically use image brightness or color information to set appropriate thresholds for separating targets from the background. When pixel values exceed or fall below specific thresholds, they are considered as targets or non-targets. These methods are simple and user-friendly, but they are less stable under changing lighting conditions and complex backgrounds. (2) Feature Engineering-Based Methods: These methods rely on manually designed features, such as texture, shape, and edges, to identify objects. Information is often extracted using filters, shape descriptors, and texture features. Subsequently, classifiers like support vector machines or decision trees are used to categorize the extracted features. (3) Template Matching: Template matching is a method for identifying objects by comparing them with predefined templates or patterns. When the similarity between the target and the template exceeds a certain threshold, it is detected as an object. This method works well when there is a high similarity between the target and the template, but it is not very robust for target rotation, scaling, and other variations. (4) Machine Learning-Based Methods: Machine learning algorithms, such as neural networks, support vector machines, and random forests, are employed to learn how to detect objects from data. Feature extraction and classifier parameters are often automatically learned from training data. This approach tends to perform well in complex object detection tasks, but it requires a significant amount of labeled data and computational resources. (5) Segmentation-Based Methods: Object detection methods based on segmentation first divide the image into target and non-target regions and then perform further analysis and classification on each region. Segmentation methods can include region growing, watershed transform, and graph cuts. This approach works well when there are clear boundaries between objects and the background. (6) Spectral Information-Based Methods: Remote sensing images typically contain information from multiple spectral bands, and this information can be leveraged for object detection. Methods based on spectral information often use spectral features like spectral reflectance and spectral angles to distinguish different types of objects. The swift advancement of deep learning technologies, particularly the introduction of convolutional neural networks (CNNs), has ushered in new possibilities and applications for object detection in remote sensing images [6; 7]. At present, the predominant frameworks for object detection in remote sensing images can be broadly classified into two major categories: single-stage and two-stage methods. Notable two-stage object detection algorithms encompass Spatial Pyramid Pooling Networks (SPP-Net) [8], Region-based CNN (R-CNN) [9], and Faster R-CNN [10]. Two-stage methods often achieve high detection accuracy but tend to be slower in terms of detection speed with larger model sizes and parameter counts, due to their two-stage nature. Single-stage detection algorithms have effectively addressed these issues, with representative algorithms such as YOLO [11], SSD [12], RetinaNet [13], CornerNet [14], and RefineDet [15].