Advanced bridge visual inspection using real-time machine learning in edge devices Article Swipe
Mahta Zakaria
,
Enes Karaaslan
,
F. Necati Çatbaş
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1186/s43251-022-00073-y
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1186/s43251-022-00073-y
Conventional methods for bridge inspection are labor intensive and highly subjective. This study introduces an optimized approach using real-time learning-based computer vision algorithms on edge devices to assist inspectors in localizing and quantifying concrete surface defects. To facilitate a better AI-human interaction, localization and quantification are separated in this study. Two separate learning-based computer vision models are selected for this purpose. The models are chosen from several available deep learning models based on their accuracy, inference speed, and memory size. For defect localization, Yolov5s shows the most promising results when compared to several other Convolutional Neural Network architectures, including EfficientDet-d0. For the defect quantification model, 12 different architectures were trained and compared. UNet with EfficientNet-b0 backbone was found to be the best performing model in terms of inference speed and accuracy. The performance of the selected model is tested on multiple edge-computing devices to evaluate its performance in real-time. This showed how different model quantization methods are considered for different edge computing devices. The proposed approach eliminates the subjectivity of human inspection and reduces labor time. It also guarantees human-verified results, generates more annotated data for AI training, and eliminates the need for post-processing. In summary, this paper introduces a novel and efficient visual inspection methodology that uses a learning-based computer vision algorithm optimized for real-time operation in edge devices (i.e., wearable devices, smartphones etc.).
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s43251-022-00073-y
- https://aben.springeropen.com/counter/pdf/10.1186/s43251-022-00073-y
- OA Status
- diamond
- Cited By
- 29
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313417289
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4313417289Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s43251-022-00073-yDigital Object Identifier
- Title
-
Advanced bridge visual inspection using real-time machine learning in edge devicesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-28Full publication date if available
- Authors
-
Mahta Zakaria, Enes Karaaslan, F. Necati ÇatbaşList of authors in order
- Landing page
-
https://doi.org/10.1186/s43251-022-00073-yPublisher landing page
- PDF URL
-
https://aben.springeropen.com/counter/pdf/10.1186/s43251-022-00073-yDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://aben.springeropen.com/counter/pdf/10.1186/s43251-022-00073-yDirect OA link when available
- Concepts
-
Computer science, Convolutional neural network, Artificial intelligence, Bridge (graph theory), Enhanced Data Rates for GSM Evolution, Edge device, Deep learning, Inference, Edge computing, Machine learning, Computer vision, Cloud computing, Operating system, Medicine, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
29Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 16, 2024: 8, 2023: 5Per-year citation counts (last 5 years)
- References (count)
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29Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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