Research Article
Research on Ship Target Detection in SAR Image Based on Improved YOLO v3 Algorithm
2 downloads
@INPROCEEDINGS{10.1007/978-3-030-90196-7_15, author={Yang Chen and Shaojie Zhu and Xiuwen Xu and Hui Ye and Yang Liu and Yuchuan Xu}, title={Research on Ship Target Detection in SAR Image Based on Improved YOLO v3 Algorithm}, proceedings={Artificial Intelligence for Communications and Networks. Third EAI International Conference, AICON 2021, Xining, China, October 23--24, 2021, Proceedings, Part I}, proceedings_a={AICON}, year={2021}, month={11}, keywords={Deep learning YOLOv3 Ship detection SAR image}, doi={10.1007/978-3-030-90196-7_15} }- Yang Chen
Shaojie Zhu
Xiuwen Xu
Hui Ye
Yang Liu
Yuchuan Xu
Year: 2021
Research on Ship Target Detection in SAR Image Based on Improved YOLO v3 Algorithm
AICON
Springer
DOI: 10.1007/978-3-030-90196-7_15
Abstract
Synthetic aperture radar (SAR) has the characteristics of all-weather, all day and multi-application observation. In recent years, ship target detection based on SAR image has been widely concerned by relevant researchers. In this paper, based on the object detection method of deep learning algorithm, the detection performance of ship target in SAR image is studied by using YOLOv3 algorithm. In order to solve the problem of increasing error rate of ship target detection in complex background, YOLOv3 algorithm is improved. By adding a preprocessing layer in the front of the input layer, the accuracy of the ship detection is improved from 92.17% to 95.80%. The algorithm can be applied to other target detection in SAR image.
Copyright © 2021–2025 ICST
