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Sensors 2018, 18(3), 737; https://doi.org/10.3390/s18030737

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement

1
Image Processing Center, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
2
State Key Laboratory of Virtual Reality Technology and Systems, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
These two authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Received: 10 November 2017 / Revised: 4 January 2018 / Accepted: 29 January 2018 / Published: 1 March 2018
(This article belongs to the Section Physical Sensors)
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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. View Full-Text
Keywords: foreign object debris; object detection; convolutional neural network; vehicular imaging sensors foreign object debris; object detection; convolutional neural network; vehicular imaging sensors
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Cao, X.; Wang, P.; Meng, C.; Bai, X.; Gong, G.; Liu, M.; Qi, J. Region Based CNN for Foreign Object Debris Detection on Airfield Pavement. Sensors 2018, 18, 737.

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