short-paper

Research on Foreign Object Debris Detection in Airport Runway Based on Semantic Segmentation

Authors:

Qiang Gao,

Ruifeng Hong,

Yutong Chen,

Jiaxing LeiAuthors Info & Claims

CONF-CDS 2021: The 2nd International Conference on Computing and Data Science

Article No.: 130, Pages 1 - 3

https://doi.org/10.1145/3448734.3450860

Published: 17 May 2021 Publication History

Get Access

Abstract

Semantic segmentation based on deep learning has very broad application scenarios in the field of computer vision. DeepLab v3+ is currently a widely used semantic segmentation framework. This paper uses the DeepLab v3+ model in TensorFlow to semantically segment foreign objects on the airport runway. Experimental results show that the semantic segmentation model based on DeepLab v3+ can accurately classify foreign objects at the pixel level, which effectively improves the accuracy of foreign object detection and reduces the risk of accidental aircraft take-off and landing.

References

[1]

Long J, Shelhamer E, Darrell T. Fully Convolutional Networks for Semantic Segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4):640-651.

Digital Library

Google Scholar

[2]

Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation[C]. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015,234-241.

Crossref

Google Scholar

[3]

Milletari F, Navab N, Ahmadi S A. v-Net: Fully Convolutional Neural Networks for volumetric Medical Image Segmentation[J]. 2016 Fourth International Conference on 3D vision (3Dv), 2016, 565-571.

Crossref

Google Scholar

[4]

Noh H, Hong S, Han B. Learning Deconvolution Network for Semantic Segmentation[C].2015 IEEE International Conference on Computer vision (ICCv), 2015, 1520-1528.

Digital Library

Google Scholar

[5]

Badrinarayanan v, Kendall A, Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481-2495.

Crossref

Google Scholar

[6]

Chen L C, Papandreou G, Kokkinos I, Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs[C]. ICLR, 2015.

Google Scholar

[7]

Chen L C, Papandreou G, Kokkinos I, DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 40(4):834-848.

Crossref

Google Scholar

[8]

Chen L C, Papandreou G, Schroff F, Rethinking Atrous Convolution for Semantic Image Segmentation [J]. arXiv preprint arXiv:1706.05587, 2018.

Google Scholar

[9]

Chen L C, Zhu Y, Papandreou G, Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation [J]. arXiv preprint arXiv:1802.02611, 2018.

Google Scholar

[10]

Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions[C]. 2017 IEEE Conference on Computer vision and Pattern Recognition (CvPR), 2017, 1800-1807.

Google Scholar

[11]

Saenpaen J, Arwatchananukul S, Aunsri N. A Comparison of Image Enhancement Methods for Lumbar Spine X-ray Image[C]// 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). 2018:798-801.

Google Scholar

Cited By

View all

G V AR P(2024)The Modern Approaches for Identifying Foreign Object Debris (FOD) in Aviation2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)10.1109/ICICACS60521.2024.10498909(1-5)Online publication date: 23-Feb-2024
https://doi.org/10.1109/ICICACS60521.2024.10498909
Wong BMarquette WBykov NPaine TBanerjee A(2023)Human-assisted robotic detection of foreign object debris inside confined spaces of marine vessels using probabilistic mappingRobotics and Autonomous Systems10.1016/j.robot.2022.104349161:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.robot.2022.104349
Farooq JMuaz MKhan Jadoon KAafaq NKhan M(2023)An improved YOLOv8 for foreign object debris detection with optimized architecture for small objectsMultimedia Tools and Applications10.1007/s11042-023-17838-w83:21(60921-60947)Online publication date: 28-Dec-2023
https://doi.org/10.1007/s11042-023-17838-w
Show More Cited By

Index Terms

Research on Foreign Object Debris Detection in Airport Runway Based on Semantic Segmentation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
  2. Machine learning
    1. Learning paradigms

Index terms have been assigned to the content through auto-classification.

Recommendations

Foreign Object Debris Detection Based on Gaussian Mixture Autoencoder of Pre-trained Features
CSAE '22: Proceedings of the 6th International Conference on Computer Science and Application Engineering

In this study, a novel anomaly localization method called Gaussian Mixture Autoencoder of Pre-trained Features (GMAPF) is proposed to perform foreign object debris (FOD) detection in the field of aviation. GMAPF utilizes the pre-trained deep ...
Research on Detection Algorithm of Foreign Object Debris and Small Targets in Airport Runway Based on SSD
CONF-CDS 2021: The 2nd International Conference on Computing and Data Science

The SSD algorithm is currently a widely used target detection algorithm. In view of the small size of common foreign objects on airport runways and the low accuracy of SSD processing small target objects, this paper applies feature fusion method in SSD ...
Performance Evaluation of LiDAR Point Clouds towards Automated FOD Detection on Airport Aprons
ATACCS '15: Proceedings of the 5th International Conference on Application and Theory of Automation in Command and Control Systems

Both the current system of airport ground control and the continuous implementation efforts of A-SMGCS and Remote Tower concepts require complete and independent surveillance coverage in real-time. We believe that 3D point clouds generated by an ...

Comments

Information & Contributors

Information

Published In

CONF-CDS 2021: The 2nd International Conference on Computing and Data Science

January 2021

1142 pages

ISBN:9781450389570

DOI:10.1145/3448734

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 May 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Conference

CONF-CDS 2021

CONF-CDS 2021: The 2nd International Conference on Computing and Data Science

January 28 - 30, 2021

CA, Stanford, USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
104
Total Downloads

Downloads (Last 12 months)13
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

G V AR P(2024)The Modern Approaches for Identifying Foreign Object Debris (FOD) in Aviation2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)10.1109/ICICACS60521.2024.10498909(1-5)Online publication date: 23-Feb-2024
https://doi.org/10.1109/ICICACS60521.2024.10498909
Wong BMarquette WBykov NPaine TBanerjee A(2023)Human-assisted robotic detection of foreign object debris inside confined spaces of marine vessels using probabilistic mappingRobotics and Autonomous Systems10.1016/j.robot.2022.104349161:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.robot.2022.104349
Farooq JMuaz MKhan Jadoon KAafaq NKhan M(2023)An improved YOLOv8 for foreign object debris detection with optimized architecture for small objectsMultimedia Tools and Applications10.1007/s11042-023-17838-w83:21(60921-60947)Online publication date: 28-Dec-2023
https://doi.org/10.1007/s11042-023-17838-w
Jing YZheng HLin CZheng WDong KLi X(2022)Foreign Object Debris Detection for Optical Imaging Sensors Based on Random ForestSensors10.3390/s2207246322:7(2463)Online publication date: 23-Mar-2022
https://doi.org/10.3390/s22072463
Jain SPrasad MGutpa R(2022)A Comparison of Manual and Automotive FOD Detection Systems at Airport Runways2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO56286.2022.9964557(1-5)Online publication date: 13-Oct-2022
https://doi.org/10.1109/ICRITO56286.2022.9964557
Shaker DAbbas A(2022)Enhancement of Foreign Object Debris Material Recognition using Deep Learning and Machine Learning2022 International Conference on Data Science and Intelligent Computing (ICDSIC)10.1109/ICDSIC56987.2022.10075879(117-122)Online publication date: 1-Nov-2022
https://doi.org/10.1109/ICDSIC56987.2022.10075879
Net MWyatt JCastano RTownes SLazio TMalphrus BKruth JHart CMattle E(2022)Enabling a Larger Deep Space Mission Suite: A Deep Space Network Queuing Antenna for Demand Access2022 IEEE Aerospace Conference (AERO)10.1109/AERO53065.2022.9843635(1-13)Online publication date: 5-Mar-2022
https://doi.org/10.1109/AERO53065.2022.9843635
Amit RMohan C(2022)GeoMask : Foreign Object Debris Instance Segmentation Using Geodesic Representations2022 IEEE Aerospace Conference (AERO)10.1109/AERO53065.2022.9843628(1-9)Online publication date: 5-Mar-2022
https://doi.org/10.1109/AERO53065.2022.9843628

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Abstract

References

Cited By

Index Terms

Recommendations

Foreign Object Debris Detection Based on Gaussian Mixture Autoencoder of Pre-trained Features

Research on Detection Algorithm of Foreign Object Debris and Small Targets in Airport Runway Based on SSD

Performance Evaluation of LiDAR Point Clouds towards Automated FOD Detection on Airport Aprons

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

HTML Format

Share

Share this Publication link

Share on social media

Affiliations