@inproceedings{81d452e181fb4ce099c130dff3cf9e14,
title = "Ship Detection from Remote Sensing Images Based on Deep Learning",
abstract = "Due to the complicated maritime climate environment, the detection of marine Ship by using Remote sensing images is faced with many challenges in the field of object detection. In this paper, a ship detection method based on dark channel priority haze removal and Faster RCNN is proposed to solve this problem. We label and experiment with thousands of ships images on the sea. Compared with the using of object detection model directly and some traditional methods, the detection accuracy of the new method is obviously improved.",
keywords = "Deep learn, Faster RCNN, Haze removal, Remote sensing",
author = "Ziqiang Yuan and Jing Geng and Tianru Dai",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Singapore Pte Ltd.; 5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017 ; Conference date: 08-12-2017 Through 10-12-2017",
year = "2018",
doi = "10.1007/978-981-13-0893-2_36",
language = "English",
isbn = "9789811308925",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "336--344",
editor = "Fuling Bian and Hanning Yuan and Jing Geng and Chuanlu Liu and Tisinee Surapunt",
booktitle = "Geo-Spatial Knowledge and Intelligence - 5th International Conference, GSKI 2017, Revised Selected Papers",
address = "Germany",
}