[1]LI Haifeng,LI Jilin,WANG Huaichao,et al.High-precision real-time detection algorithm for foreign object debris on complex airport pavements[J].CAAI Transactions on Intelligent Systems,2023,18(3):525-533.[doi:10.11992/tis.202110014]
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High-precision real-time detection algorithm for foreign object debris on complex airport pavements

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 3 Page number: 525-533 Column: 学术论文—智能系统 Public date: 2023-07-05
Title:
High-precision real-time detection algorithm for foreign object debris on complex airport pavements
Author(s):
LI Haifeng1;  LI Jilin1;  WANG Huaichao1;  GUI Zhongcheng2
1. College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China;
2. Chengdu Guimu Robot Co., Ltd., Chengdu 610310, China
Keywords:
foreign objects debris on airport pavement; small target detection; multi-scale fusion; texture information extraction; super-resolution; subpixel convolution; feature extraction; complete intersection ratio
CLC:
TP391
DOI:
10.11992/tis.202110014
Abstract:
Foreign object debris (FOD) on the airport pavement has the characteristics of diverse types, different shapes, complex backgrounds, and weak targets, which seriously affect safety of aircrafts. Therefore, high-precision real-time detection of FOD on the airport pavement is of great significance. To solve the above problems, we propose a FOD real-time detection network (FOD-RDN) based on super-resolution feature pyramid with texture information extraction module. The network uses Darknet-53 as the backbone network to extract features, and detects small targets with different shapes through super-resolution feature pyramid. Then the texture information extraction module is designed to reduce the interference of complex background. At the same time, a dual-channel YOLO detector and a CIoU-based loss function are used to further improve the accuracy and speed of the network detecting FOD. The experimental results show that the algorithm in this paper can achieve an overall detection accuracy of 91.8% on the FOD dataset under the condition of meeting the real-time requirements, which is better than the mainstream object detection network in terms of FOD detection.
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