# SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection
## 1 Introduction  
Remote sensing images play an essential role in various fields, such as real-time aviation and navigation monitoring, ecological resource and environmental monitoring, military object detection, and geological disaster detection [1; 2]. However, due to limitations in resolution and the distance between the remote sensing equipment and the objects being observed, the captured images often depict the target objects in a tiny form. These tiny objects, characterized by their small scale and weak features, present significant challenges for accurate object detection in remote sensing images. Consequently, the detection of tiny objects in remote-sensing images has emerged as a major research problem in the field of computer vision. Specifically, this problem revolves around detecting and recognizing small-sized objects in images or videos. For instance, in the commonly used COCO dataset [3], objects with resolutions smaller than 32 \(\times\) 32 pixels are classified as tiny objects. In early object detection research, most methods are based on traditional feature extraction methods (e.g., shape, color, texture, etc. ), determining object candidate regions through sliding windows of different scales and inputting object features such as Haar features [4],HOG features [5], and SIFT [6] features into the classifier to classify the candidate objects within the regions. Nevertheless, conventional object detection algorithms have various limitations, such as inefficient candidate region selection and window redundancy. These drawbacks hinder the detection of tiny targets in remote sensing images [7; 8], and the traditional object detection algorithms are only applicable to images with simple backgrounds and significant features, which have great limitations in practical applications. With the rapid development of computers and deep learning [9; 10], Many researchers have employed sophisticated deep learning models to implement object detection in remotely sensed images. Compared with earlier object detection methods, object detection using deep learning methods has features such as feature self-extraction, high adaptability and robustness, high accuracy, and higher efficiency in processing large-scale data, such as R-CNN [11], Fast R-CNN [12], Faster R-CNN [13], SSD [14], YOLO [15], RetinaNet [16], CenterNet [17], etc. These algorithms usually use convolutional neural networks (CNNs) to extract image features and perform detection operations on the feature maps. These methodologies can be categorized as two-stage approaches, relying on the extraction of regions as their foundation. (e.g., R-CNN, Fast R-CNN, Faster R-CNN, etc.) and one-stage methods based on regression (e.g., YOLO, SSD, RetinaNet, etc. ), as well as keypoint-based methods (e.g., CenterNet, etc.). One-stage detection algorithms straightforwardly predict the object's location and class from the input image, eliminating the need for an explicit candidate region extraction phase. While computationally smaller and faster, they tend to be less accurate than two-stage detection algorithms. In two-stage algorithms, the candidate region extraction phase involves techniques such as Selective Search [18] and RPN [19] to generate regions potentially containing objects. Although more computationally demanding and slower due to explicit region extraction, two-stage detection algorithms improve object recognition accuracy, especially for objects with complex backgrounds. Unlike the previous two algorithms, the keypoint-based approach uses the idea of anchor-free, eliminating the need for anchors when training the model, thus simplifying the training process, but still does not provide a remarkable trade-off between precision and speed. At present, there remains significant room for improvement in the accuracy of detecting tiny objects in remote sensing images. The challenges of detecting small objects arise from their low resolution, which often confuses the surrounding background.