摘要
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.
| 源语言 | 英语 |
|---|---|
| 主期刊名 | 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering |
| 编辑 | Tao Zhang |
| 出版商 | SPIE |
| ISBN（电子版） | 9781510656437 |
| DOI | |
| 出版状态 | 已出版 - 2022 |
| 活动 | 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering - Xishuangbanna, 中国 期限: 18 3月 2022 → 20 3月 2022 |
出版系列
| 姓名 | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| 卷 | 12294 |
| ISSN（印刷版） | 0277-786X |
| ISSN（电子版） | 1996-756X |
会议
| 会议 | 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering |
|---|---|
| 国家/地区 | 中国 |
| 市 | Xishuangbanna |
| 时期 | 18/03/22 → 20/03/22 |
指纹
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Li, K., Cao, Y., & Chen, H. (2022). Remote Sensing Object Detection Based on Lightweight YOLO-V4. 在 T. Zhang (编辑), 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering 文章 122944O (Proceedings of SPIE - The International Society for Optical Engineering; 卷 12294). SPIE. https://doi.org/10.1117/12.2639675