Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18030737 | SENSORS |
Keywords | Field | DocType |
foreign object debris,object detection,convolutional neural network,vehicular imaging sensors | Computer vision,Object detection,Foreign object damage,Convolutional neural network,Transformer,Electronic engineering,Artificial intelligence,Engineering,Classifier (linguistics),Optical imaging,Detector | Journal |
Volume | Issue | Citations |
18 | 3.0 | 2 |
PageRank | References | Authors |
0.44 | 12 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoguang Cao | 1 | 3 | 1.13 |
| Peng Wang | 2 | 385 | 106.03 |
| Cai Meng | 3 | 4 | 1.62 |
| Xiangzhi Bai | 4 | 339 | 33.81 |
| Guoping Gong | 5 | 2 | 0.44 |
| Miaoming Liu | 6 | 2 | 0.78 |
| Jun Qi | 7 | 68 | 7.68 |