# A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8
## 1 Introduction  
Remote sensing image target detection aims to identify specific target objects in remote sensing images, including the identification of feature objects and location determination. Remote sensing image target detection has a wide range of applications in the field of land resource management, national defense, and military and is a current research hotspot in the field of remote sensing image processing [1]. In recent years, with the continuous development of remote sensing sensors, the spatial resolution and spectral resolution of remote sensing images have continued to improve, and the information contained in images has become increasingly complex. Remote sensing images generally have a high level of background complexity, a large image size, a dense distribution of targets, and a large number of small targets, leading to an increase in the difficulty of target detection in remote sensing images. Traditional target detection methods mostly realize the detection of image information by means of feature extraction, such as histograms of oriented gradients (HOGs) [2], scale-invariant feature transform (SIFT) [3], and local binary pattern histograms (LBPHs) [4]. These traditional methods have certain advantages in terms of extracting local features of the image and the design of classifiers, but they tend not to be well differentiated for the detection of targets in complex scenes, and they cannot effectively detect diverse geo-targets. In recent years, with the significant improvement of computer arithmetic power, deep learning has become a much-anticipated research direction in the field of artificial intelligence. Among deep learning algorithms, target detection algorithms based on convolutional neural networks (CNNs) have achieved faster operation speed and higher detection accuracy due to their weight sharing and invariance of translatability, gradually replacing traditional target detection algorithms and becoming the currentmainstream target detection method. Target detection algorithms based on deep learning can be mainly categorized into two types: two-stage detection and single-stage detection. Two-stage detection first extracts features from the image in the detection process and generates some candidate regions, then outputs the location and category information of the target after classifying and localizing the candidate regions. Typical two-stage detection algorithms include R-CNN [5], Fast R-CNN [6], and Faster R-CNN [7]. Single-stage detection uses a CNN for image feature extraction and passes the extracted features to multiple fully connected layers for target detection, directly outputting the location and category information of the target. Typical single-stage detection algorithms include the YOLO (you only look once) series [8; 9; 10; 11] and SSD (single-shot multibox detector) [12; 13]. The fact that a single-stage detection algorithm can complete the processing and analysis of the information in the image without pre-generating candidate regions eliminates the need for frequent data conversion and computation, considerably improving the efficiency of the detection. Therefore, single-stage detection algorithms are more widely used than two-stage detection algorithms for large-volume data processing. Although single-stage detection algorithms represented by the YOLO series can achieve better performance than many target detection methods, remote sensing images are generally characterized by a high proportion of background information, a high degree of target aggregation, and a large number of small targets, making the detection accuracy of the YOLO series lower than that of two-stage detection algorithms. In order to enhance the detection performance of targets in remote sensing images, many researchers have made improvements to the YOLO series. Reference [14] introduces YOLO-SE, a novel YOLOv8-based network. The authors proposed the SEF module, an enhancement based on SEConv, to tackle multi-scale object detection.