Detecting of foreign object debris on airfield pavement using convolution neural network

Xiaoguang Cao; Yufeng Gu; Xiangzhi Bai

doi:10.1117/12.2295282

15 November 2017 Detecting of foreign object debris on airfield pavement using convolution neural network

Xiaoguang Cao, Yufeng Gu, Xiangzhi Bai

Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 1060536 (2017) https://doi.org/10.1117/12.2295282
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China

Abstract

It is of great practical significance to detect foreign object debris (FOD) timely and accurately on the airfield pavement, because the FOD is a fatal threaten for runway safety in airport. In this paper, a new FOD detection framework based on Single Shot MultiBox Detector (SSD) is proposed. Two strategies include making the detection network lighter and using dilated convolution, which are proposed to better solve the FOD detection problem. The advantages mainly include: (i) the network structure becomes lighter to speed up detection task and enhance detection accuracy; (ii) dilated convolution is applied in network structure to handle smaller FOD. Thus, we get a faster and more accurate detection system.

Citation Download Citation

Xiaoguang Cao, Yufeng Gu, and Xiangzhi Bai "Detecting of foreign object debris on airfield pavement using convolution neural network", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060536 (15 November 2017); https://doi.org/10.1117/12.2295282

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