Details
| Original language | English |
|---|---|
| Pages (from-to) | 301 - 308 |
| Number of pages | 8 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 48 |
| Issue number | 1 |
| Publication status | Published - 21 Apr 2023 |
| Event | 39th International Symposium of Remote Sensing on Enviroment: "From Human needs to SDGs" - Duration: 24 Apr 2023 → 27 Apr 2023 https://isrse39.com/Default.aspx |
Abstract
Regular inspection and maintenance of infrastructure facilities are crucial to ensure their functionality and safety for users. However, current inspection methods are labor-intensive and can vary depending on the inspector. To improve this process, modern sensor systems and machine learning algorithms can be deployed to detect defects based on rapidly acquired data, resulting in lower downtime. A quality-controlled processing chain allows to provide hence informed uncertainty assessments to inspection operators. In this study, we present several Deeplab V3+ models optimized to predict corroded segments of the quay wall at JadeWeserPort, Germany, which is a dataset from the 3D HydroMapper research project. Our models achieve generally high accuracy in detecting this damage type. Therefore, we examine the use of a Region Growing-based weakly supervised approach to efficiently extend our model to other common types in the future. This approach achieves about 90 % of the results compared to corresponding fully supervised networks, of which a ResNet-50 variant peaks at 55.6 % Intersection-over-Union regarding the test set's corrosion class.
Keywords
- Image segmentation, Damage Detection, Deep Learning, Optimization, Supervised, Weakly Supervised, Image segmentation, Damage Detection, Weakly Supervised, Supervised, Deep Learning, Optimization
ASJC Scopus subject areas
- Social Sciences(all)
- Geography, Planning and Development
- Computer Science(all)
- Information Systems
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In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 48, No. 1, 21.04.2023, p. 301 - 308.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Adaption of deeplab V3+ for damage detection on port infrastructure imagery
AU - Scherff, Marvin
AU - Hake, Frederic
AU - Alkhatib, Hamza
N1 - Funding Information: Funding This research was funded by the Deutsche Forschungs-gemeinschaft (DFG, German Research Foundation) - NE 1453/5-1 and as part of the Research Training Group i.c.sens [RTG 2159].
PY - 2023/4/21
Y1 - 2023/4/21
N2 - Regular inspection and maintenance of infrastructure facilities are crucial to ensure their functionality and safety for users. However, current inspection methods are labor-intensive and can vary depending on the inspector. To improve this process, modern sensor systems and machine learning algorithms can be deployed to detect defects based on rapidly acquired data, resulting in lower downtime. A quality-controlled processing chain allows to provide hence informed uncertainty assessments to inspection operators. In this study, we present several Deeplab V3+ models optimized to predict corroded segments of the quay wall at JadeWeserPort, Germany, which is a dataset from the 3D HydroMapper research project. Our models achieve generally high accuracy in detecting this damage type. Therefore, we examine the use of a Region Growing-based weakly supervised approach to efficiently extend our model to other common types in the future. This approach achieves about 90 % of the results compared to corresponding fully supervised networks, of which a ResNet-50 variant peaks at 55.6 % Intersection-over-Union regarding the test set's corrosion class.
AB - Regular inspection and maintenance of infrastructure facilities are crucial to ensure their functionality and safety for users. However, current inspection methods are labor-intensive and can vary depending on the inspector. To improve this process, modern sensor systems and machine learning algorithms can be deployed to detect defects based on rapidly acquired data, resulting in lower downtime. A quality-controlled processing chain allows to provide hence informed uncertainty assessments to inspection operators. In this study, we present several Deeplab V3+ models optimized to predict corroded segments of the quay wall at JadeWeserPort, Germany, which is a dataset from the 3D HydroMapper research project. Our models achieve generally high accuracy in detecting this damage type. Therefore, we examine the use of a Region Growing-based weakly supervised approach to efficiently extend our model to other common types in the future. This approach achieves about 90 % of the results compared to corresponding fully supervised networks, of which a ResNet-50 variant peaks at 55.6 % Intersection-over-Union regarding the test set's corrosion class.
KW - Image segmentation, Damage Detection, Deep Learning, Optimization, Supervised, Weakly Supervised
KW - Image segmentation
KW - Damage Detection
KW - Weakly Supervised
KW - Supervised
KW - Deep Learning
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85156203394&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-XLVIII-M-1-2023-301-2023
DO - 10.5194/isprs-archives-XLVIII-M-1-2023-301-2023
M3 - Conference article
VL - 48
SP - 301
EP - 308
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
IS - 1
T2 - 39th International Symposium of Remote Sensing on Enviroment
Y2 - 24 April 2023 through 27 April 2023
ER -