@inproceedings{317fdb3a62874c189793198689305332,
title = "Remote Sensing Object Detection Based on Lightweight YOLO-V4",
abstract = "The remote sensing object detection algorithms based on deep learning have high detection performances, but the network structures are too complexity to meet the real-time processing requirements in on-board remote sensing object detection. In order to solve this problem, we proposed a lightweight YOLO-v4 network, which is 76% smaller than the original YOLO-v4. As for the decrease of lightweight network's accuracy, we adopted the general instance distillation algorithm, which used the original YOLO-v4 network as the teacher network and whose detection accuracy achieved 2.1% mAP gain.",
keywords = "Remote sensing, deep learning, lightweight network, object detection",
author = "Keng Li and Yunfei Cao and He Chen",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE. All rights reserved.; 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering ; Conference date: 18-03-2022 Through 20-03-2022",
year = "2022",
doi = "10.1117/12.2639675",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Tao Zhang",
booktitle = "7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering",
address = "United States",
}