Fast Automatic Vehicle Detection in UAV Images Using Convolutional Neural Networks
| Autor: | Huijie Zhang, Jian Zhang, Haitao Jia, Geng Leng, Xiaoyue Tian, Wang Meng, Luo Xin, He Xixu, Xu Wenbo, Weimin Hou |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
K-means++
Artificial neural network business.industry Computer science Science 020206 networking & telecommunications 02 engineering and technology YOLOv3 Convolutional neural network Image (mathematics) UAV images Vehicle detection Soft-NMS 0202 electrical engineering electronic engineering information engineering vehicle detection General Earth and Planetary Sciences 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | Remote Sensing Volume 12 Issue 12 Pages: 1994 Remote Sensing, Vol 12, Iss 1994, p 1994 (2020) |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs12121994 |
| Popis: | Vehicle targets in unmanned aerial vehicle (UAV) images are generally small, so a significant amount of detailed information on targets may be lost after neural computing, which leads to the poor performances of the existing recognition algorithms. Based on convolutional neural networks that utilize the YOLOv3 algorithm, this article focuses on the development of a quick automatic vehicle detection method for UAV images. First, a vehicle dataset for target recognition is constructed. Then, a novel YOLOv3 vehicle detection framework is proposed according to the following characteristics: The vehicle targets in the UAV image are relatively small and dense. The average precision (AP) increased by 5.48%, from 92.01% to 97.49%, which still remains the rather high processing speed of the YOLO network. Finally, the proposed framework is tested using three datasets: COWC, VEDAI, and CAR. The experimental results demonstrate that our method had a better detection capability. |
| Databáze: | OpenAIRE |
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