@inproceedings{c1588b473d5444e4a5315cfc8f6a7e91,
title = "SCSF-NET: SINGLE CLASS SCALE FIXED NETWORK FOR OBJECT DETECTION IN OPTICAL REMOTE SENSING IMAGES ON LIMITED HARDWARE",
abstract = "The detection of objects such as vehicle, airplane and ship is a fundamental problem in optical remote-sensing(ORS) image process. Despite a great success has achieved by migrating nature image detection methods to the remote sensing field, some challenges in hardware limit environments still remain to be solved, e.g., space-borne hardware and UAV-borne hardware. We proposed a low-computational network by digging several prior knowledge in the remote sensing field. By focusing on certain ground sample distance(gsd) and single target class, the proposed method gains high performance with only less than 1\% parameters and less than 1\% computation used comparing with the state-of-the-art detection method. Detection result on public available vehicle dataset demonstrates the effectiveness of the proposed method. Meanwhile, the ship and airplane detection results of two private datasets are also shown. Our vehicle detection code on limited hardware is now available at https://github.com/minghuicode/scsf-detector.",
keywords = "Deep learning, Limit hardware, Optical remote sensing(ORS) image, Small object detection, Vehicle detection",
author = "Minghui Wang and Qingpeng Li and Junjun Pan and Yunchao Gu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 ; Conference date: 12-07-2021 Through 16-07-2021",
year = "2021",
doi = "10.1109/IGARSS47720.2021.9553056",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4184--4187",
booktitle = "IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "美国",
}