# KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images
## 1 Introduction  
In recent years, with the continuous development of wireless communication, materials science, artificial intelligence, track analysis, and other technologies, the amount of valuable information in aerial remote sensing images has increased significantly. With the improvement of imaging quality, the multi-scale object which exists in the image contains more detailed and strategic information; some multi-scale targets in aerial remote sensing images such as vehicles, civil facilities, and aircraft can also be found easily by the search equipment, for which the aerial remote sensing images have become a hot spot for scholars in geology [1], agricultural [2], military [3], and forestry [4]. In the era of artificial intelligence, the economic and strategic values of aerial remote sensing images are gradually reflected. For example, [PERSON] et al. [5] proposed an improved algorithm based on Mask RCNN to improve the accuracy of identifying special geological structures, and the unique geographical structure can be protected to achieve the role of disaster reduction. [PERSON] et al. [6] used an object detection algorithm based on visual text information to search for the point of the gas leakage in aerial remote sensing images, their research can alleviate environmental pollution and prevent economic losses. Therefore, the high-precision detection method of multi-scale objects in aerial remote sensing images has high research value. On the research of the object detection, the high-precision detection of multi-scale objects in aerial remote sensing images remains a formidable challenge. First, the multi-scale object is readily disturbed by environmental factors such as light intensity, topographic influences, and prevailing climatic conditions in the background of the aerial remote sensing images, objects are easily ignored by detection methods. Second, the ability of object feature extraction and fusion of existing detection methods still have room to be improved, because the loss of feature information will lead to a decrease in detection accuracy. Third, due to the large size of aerial remote sensing images, the feature of the tiny object is easily covered by the feature of the large object, which will result in the reduction of detection accuracy, if the algorithm pays too much attention to the object with large size, the detection accuracy of objects that have tiny sizes will be reduced. Fourth, the existing loss function usually ignores the vector angle between the ground truth box and the prediction box, which affects the detection accuracy in the detection task. The detection ability of the traditional object detection algorithm based on the template matching method [7] is not efficient, because this method needs to manually extract and classify object features, its speed and accuracy are difficult to guarantee when dealing with a large number of tiny objects. Machine learning algorithms [8] often need to describe a large number of features by complex mathematical statistical methods, and their detection performance and generalization ability are obviously insufficient. With the development of deep learning methods in the research of object detection, a large number of object detection methods based on deep neural networks have been proposed. Among them, the representative two-stage algorithm are R-CNN serial algorithms [9; 10; 11], while the representative single-stage algorithms are YOLO serial algorithms [12; 13; 14; 15; 16; 17] and SSD serial algorithms [18; 19]. The emergence of these algorithms based on deep neural networks has greatly improved the performance of object detection. The aerial remote sensing image as a kind of image is also used in the research of object detection. [PERSON] et al. [20] proposed an improved Faster-RCNN algorithm to improve the recognition accuracy of mineral resources. [PERSON] et al. [21] improved the detection precision of aircraft objects in remote sensing images by reducing the feature fusion output ports of neck network in YOLO algorithm.