| Robust vehicle detection in aerial images based on cascaded convolutional neural networks | |
| Zhong, Jiandan1,2,3; Lei, Tao1; Yao, Guangle1,2,3 | |
| Source Publication | Sensors (Switzerland)
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| Volume | 17Issue:12Pages:2720 |
| 2017 | |
| Language | 英语 |
| ISSN | 1424-8220 |
| Indexed By | Ei |
| Abstract | Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed. © 2017 by the authors. Licensee MDPI, Basel, Switzerland. |
| Keyword | Aerial photography - Convolution - Decision making - Deep learning - Image processing - Neural networks - Object detection - Object recognition - Vehicles |
| Document Type | 期刊论文 |
| Identifier | http://ir.ioe.ac.cn/handle/181551/8897 |
| Collection | 光电测控技术研究室（三室） |
| Affiliation | 1.Institute of Optics and Electronics, Chinese Academy of Sciences, No. 1, Guangdian Avenue, Chengdu; 610209, China; 2.School of Optoelectronic Information, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu; 610054, China; 3.University of Chinese Academy of Sciences, 19 A Yuquan Rd, Shijingshan District, Beijing; 100039, China |
| Recommended Citation GB/T 7714 | Zhong, Jiandan,Lei, Tao,Yao, Guangle. Robust vehicle detection in aerial images based on cascaded convolutional neural networks[J]. Sensors (Switzerland),2017,17(12):2720. |
| APA | Zhong, Jiandan,Lei, Tao,&Yao, Guangle.(2017).Robust vehicle detection in aerial images based on cascaded convolutional neural networks.Sensors (Switzerland),17(12),2720. |
| MLA | Zhong, Jiandan,et al."Robust vehicle detection in aerial images based on cascaded convolutional neural networks".Sensors (Switzerland) 17.12(2017):2720. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 2017-2137.pdf（7314KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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