Research on Spaceborne Target Detection Based on Yolov5 and Image Compression

Qi Shi, Daheng Wang, Wen Chen, Jinpei Yu (), Weiting Zhou, Jun Zou and Guangzu Liu
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Qi Shi: Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, China
Daheng Wang: China Satellite Network Group Co., Ltd., Beijing 100000, China
Wen Chen: Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, China
Jinpei Yu: Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201304, China
Weiting Zhou: School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Jun Zou: School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Guangzu Liu: School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Future Internet, 2023, vol. 15, issue 3, 1-17

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.

Keywords: target detection; image compression; Yolov5; remote sensing (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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