# Research on Spaceborne Target Detection Based on Yolov5 and Image Compression
###### Abstract  
Satellite image compression technology plays an important role in the development of space science. As optical sensors on satellites become more sophisticated, high-resolution and high-fidelity satellite images will occupy more storage. This raises the required transmission bandwidth and transmission rate in the satellite-ground data transmission system. In order to reduce the pressure from image transmission on the data transmission system, a spaceborne target detection system based on Yolov5 and a satellite image compression transmission system is proposed in this paper. It can reduce the pressure on the data transmission system by detecting the object of interest and deciding whether to transmit. An improved Yolov5 network is proposed to detect the small target on the high-resolution satellite image. Simulation results show that the improved Yolov5 network proposed in this paper can detect specific targets in real satellite images, including aircraft, ships, etc. At the same time, image compression has little effect on target detection, so detection complexity can be effectively reduced and detection speed can be improved by detecting the compressed images. target detection; image compression; Yolov5; remote sensing 202315114.11.11.11.11.11.11.11.11.11.11.11.1.11.11.11.1.11.1.11.1.11.1.11.1.11.1.1.1.1.11.1.  
airplanes, ships, offshore beaches, and islands. For such targets, the main mission requirements are location annotation and mask segmentation of ships, ports, islands, and aircraft in the image, against the background of a sea surface, a lake surface, or the ground. Aircraft and ship images are different from natural images in scale and resolution, and ports and islands are not obvious because of their rugged contours. The compression is usually used to decrease the image size and the pressure on the communication link between the satellite and the ground. We can use target detection to further decrease the pressure on the communication link and let the satellite only transmit the image we are interested in. Traditional target detection generally adopts a rotating target detection method, which can be divided into two main technical routes: one-stage and two-stage detection [7; 8; 9]. The one-stage rotating target detection method is based on one-stage horizontal target detection methods such as Yolo [10] and SSD [11]. The target expression in the probe head is replaced by the rotating target expression, and the design for the characteristics of the rotating target is added. For example, TextBoxes++ uses a \(3\times 5\) convolution kernel with a larger aspect ratio to extract the features of a rotating target with a larger aspect ratio in the feature extraction stage [12]. Because of the large length and width ratio, the rotation target is arranged more closely than the ordinary target, so TextBoxes++ implements a denser prior box. In RSR [13], the rotation-sensitive convolution layer is used to replace the common convolution layer, and the features with rotation are extracted, which is more suitable for the changeable characteristics of the rotating target. Moreover, an inception module is added to deal with rotating targets with large aspect ratios. The two-stage rotating target detection method is based on two-stage horizontal target detection methods, such as Faster R-CNN. The traditional methods, such as RR-CNN [14], directly increase the angle prediction in the final regression stage to predict the rotation of the target box. For example, the RRPN [15], R2 PN [16], and RR-CNN methods have similar technical routes. Angle parameters are generally added to anchor the frame design, and rotating candidate frames are proposed in the stage of the candidate frame. Then, the features of the rotating candidate frames are extracted by the pooling of the rotating RoI (region of interest). Finally, the regression target boxes and categories are utilized. Most of the late two-stage rotating target detection methods are based on an RoI transformer [15].