@inproceedings{7b474bca022e45d29710811caa7f68bc,
title = "An Improved YOLO-v3Algorithm for Ship Detection in SAR Image Based on K-means++ with Focal Loss",
abstract = "In recent years, practical industrial production application have put forward extremely high requirements for its detection accuracy and detection efficiency in the aspect of synthetic aperture radar (SAR) image ship detection. Among the solutions to this problem, the YOLO has received more and more attention due to its advantages such as high speed. In this paper, the K-means++ is used to obtain the Anchor Box, the Focal loss is introduced to balance the proportion of positive and negative samples, and an improved image detection algorithm based on YOLO-v3 is proposed to solve the low detection efficiency and detection accuracy of ship images. The experimental results show that the improved algorithm in this paper can get rid of the local optimum well, shorten the convergence time, improve the training efficiency and the accuracy of ship image detection.",
keywords = "Focal loss, K-means++, ship detection, YOLO-v3",
author = "Haonan Wang and Baolong Wu and Yanni Wu and Shuangxi Zhang and Shaohui Mei and Yanyang Liu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 3rd China International SAR Symposium, CISS 2022 ; Conference date: 02-11-2022 Through 04-11-2022",
year = "2022",
doi = "10.1109/CISS57580.2022.9971239",
language = "英语",
series = "3rd China International SAR Symposium, CISS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "3rd China International SAR Symposium, CISS 2022",
}