An improved YOLO V3 for small vehicles detection in aerial images
文献类型：会议论文
| 作者 | Ju MR(鞠默然)1,2,3,4,5 ; Luo HB(罗海波)1,2,3,4,5; Wang ZB(王仲博)1,2,3,4,5
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| 出版日期 | 2020 |
| 会议日期 | December 24-26, 2020 |
| 会议地点 | Sanya, China |
| 关键词 | Convolutional neural network Small vehicle detection YOLO V3 Aerial image |
| 页码 | 1-5 |
| 英文摘要 | Small vehicle detection in aerial images is a challenge in computer vision because small vehicles occupy less pixels and the environment around the small vehicles is complex. To improve the detection performance for the vehicles in aerial images, we propose an improved YOLO V3. The main contributions of our work include: (1) We redesign the backbone of YOLO V3 to select suitable scales for small vehicle detection in aerial images; (2) To make the improved YOLO V3 much stronger, we redesign the loss function of original YOLO V3 by GIOU loss and Focal loss; (3) To verify the performance of improved YOLO V3, we do the comparative experiments on VEDAI dataset. The experimental results show that the proposed method has obtained better performance than original YOLO V3 for small vehicle detection in aerial image. |
| 产权排序 | 1 |
| 会议录 | ACAI 2020: 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence
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| 会议录出版者 | ACM |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-4503-8811-5 |
| 源URL | [http://ir.sia.cn/handle/173321/28699]  |
| 专题 | 沈阳自动化研究所_光电信息技术研究室 |
| 通讯作者 | Ju MR(鞠默然) |
| 作者单位 | 1.Shenyang Institute of Automation, Chinese Academy of Sciences 2.The Key Laboratory of Image Understanding and Computer Vision 3.University of Chinese Academy of Sciences 4.Key Laboratory of Opt-Electronic Information Processing, Chinese Academy of Sciences 5.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Ju MR,Luo HB,Wang ZB. An improved YOLO V3 for small vehicles detection in aerial images[C]. 见:. Sanya, China. December 24-26, 2020. |
入库方式： OAI收割
来源：沈阳自动化研究所
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