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A Lightweight Improved YOLOv8 for SAR-based Ship Detection in ICRS LEO Satellite Systems
- Lee, Younggyu;
- Park, Jaewoo;
- Im, Gyeongrae;
- Kang, Jinho
SCOPUS
0초록
Recently, integrated communication and remote sensing (ICRS) based LEO satellite systems have received great attention. To overcome the limited resources such as restricted battery capacity in the LEO satellite systems, lightweight deep neural network (DNN) models for object detection are essential. In this paper, we develop an improved YOLOv8s for Synthetic Aperture Radar (SAR)-based ship detection to enhance the computational efficiency in the LEO satellite systems, by replacing specific convolution layers with Spatial-Channel Decoupled Downsampling (SCDown) modules. On the SSDD dataset, it reduces GFLOPs and parameters and improves inference speed by 28.7 FPS while maintaining detection performance compared to the original YOLOv8s. © 2025 IEEE.
키워드
- 제목
- A Lightweight Improved YOLOv8 for SAR-based Ship Detection in ICRS LEO Satellite Systems
- 저자
- Lee, Younggyu; Park, Jaewoo; Im, Gyeongrae; Kang, Jinho
- 발행일
- 2026-02
- 유형
- Conference paper
- 저널명
- International Conference on ICT Convergence
- 페이지
- 2036 ~ 2037