期刊
SENSORS
卷 18, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/s18030737
关键词
foreign object debris; object detection; convolutional neural network; vehicular imaging sensors
资金
- National Natural Science Foundation of China [U1736217]
- Program for New Century Excellent Talents in Universities [NCET-13-0020]
- Fundamental Research Funds for the Central Universities [YWF-17-BJ-Y-69]
In this paper, a novel algorithm based on convolutional neural network (CNN) is proposed to detect foreign object debris (FOD) based on optical imaging sensors. It contains two modules, the improved region proposal network (RPN) and spatial transformer network (STN) based CNN classifier. In the improved RPN, some extra select rules are designed and deployed to generate high quality candidates with fewer numbers. Moreover, the efficiency of CNN detector is significantly improved by introducing STN layer. Compared to faster R-CNN and single shot multiBox detector (SSD), the proposed algorithm achieves better result for FOD detection on airfield pavement in the experiment.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据