AI‐enabled airport runway pavement distress detection using dashcam imagery Article Swipe
Arman Malekloo
,
Xiaoyue Cathy Liu
,
David Sacharny
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1111/mice.13200
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1111/mice.13200
Maintaining airport runways is crucial for safety and efficiency, yet traditional monitoring relies on manual inspections, prone to time consumption and inaccuracy. This study pioneers the utilization of low‐cost dashcam imagery for the detection and geolocation of airport runway pavement distresses, employing novel deep‐learning frameworks. A significant contribution of our work is the creation of the first public dataset specifically designed for this purpose, addressing a critical gap in the field. This dataset, enriched with diverse distress types under various environmental conditions, enables the development of an automated, cost‐effective method that substantially enhances airport maintenance operations. Leveraging low‐cost dashcam technology in this unique scenario, our approach demonstrates remarkable potential in improving the efficiency and safety of airport runway inspections, offering a scalable solution for infrastructure management. Our findings underscore the benefits of integrating advanced imaging and artificial intelligence technologies, paving the way for advancements in airport maintenance practices.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/mice.13200
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/mice.13200
- OA Status
- hybrid
- Cited By
- 8
- References
- 74
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393987637
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4393987637Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/mice.13200Digital Object Identifier
- Title
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AI‐enabled airport runway pavement distress detection using dashcam imageryWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-04Full publication date if available
- Authors
-
Arman Malekloo, Xiaoyue Cathy Liu, David SacharnyList of authors in order
- Landing page
-
https://doi.org/10.1111/mice.13200Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/mice.13200Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/mice.13200Direct OA link when available
- Concepts
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Runway, Distress, Environmental science, Computer science, Remote sensing, Transport engineering, Engineering, Geology, Cartography, Psychology, Geography, Clinical psychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8Per-year citation counts (last 5 years)
- References (count)
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74Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.types | 78 |
| abstract_inverted_index.under | 79 |
| abstract_inverted_index.field. | 71 |
| abstract_inverted_index.manual | 15 |
| abstract_inverted_index.method | 90 |
| abstract_inverted_index.paving | 140 |
| abstract_inverted_index.public | 58 |
| abstract_inverted_index.relies | 13 |
| abstract_inverted_index.runway | 39, 118 |
| abstract_inverted_index.safety | 7, 115 |
| abstract_inverted_index.unique | 103 |
| abstract_inverted_index.airport | 2, 38, 94, 117, 146 |
| abstract_inverted_index.crucial | 5 |
| abstract_inverted_index.dashcam | 30, 99 |
| abstract_inverted_index.dataset | 59 |
| abstract_inverted_index.diverse | 76 |
| abstract_inverted_index.enables | 83 |
| abstract_inverted_index.imagery | 31 |
| abstract_inverted_index.imaging | 135 |
| abstract_inverted_index.runways | 3 |
| abstract_inverted_index.various | 80 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.advanced | 134 |
| abstract_inverted_index.approach | 106 |
| abstract_inverted_index.benefits | 131 |
| abstract_inverted_index.creation | 54 |
| abstract_inverted_index.critical | 67 |
| abstract_inverted_index.dataset, | 73 |
| abstract_inverted_index.designed | 61 |
| abstract_inverted_index.distress | 77 |
| abstract_inverted_index.enhances | 93 |
| abstract_inverted_index.enriched | 74 |
| abstract_inverted_index.findings | 128 |
| abstract_inverted_index.offering | 120 |
| abstract_inverted_index.pavement | 40 |
| abstract_inverted_index.pioneers | 25 |
| abstract_inverted_index.purpose, | 64 |
| abstract_inverted_index.scalable | 122 |
| abstract_inverted_index.solution | 123 |
| abstract_inverted_index.detection | 34 |
| abstract_inverted_index.employing | 42 |
| abstract_inverted_index.improving | 111 |
| abstract_inverted_index.potential | 109 |
| abstract_inverted_index.scenario, | 104 |
| abstract_inverted_index.Leveraging | 97 |
| abstract_inverted_index.addressing | 65 |
| abstract_inverted_index.artificial | 137 |
| abstract_inverted_index.automated, | 88 |
| abstract_inverted_index.efficiency | 113 |
| abstract_inverted_index.low‐cost | 29, 98 |
| abstract_inverted_index.monitoring | 12 |
| abstract_inverted_index.practices. | 148 |
| abstract_inverted_index.remarkable | 108 |
| abstract_inverted_index.technology | 100 |
| abstract_inverted_index.underscore | 129 |
| abstract_inverted_index.Maintaining | 1 |
| abstract_inverted_index.conditions, | 82 |
| abstract_inverted_index.consumption | 20 |
| abstract_inverted_index.development | 85 |
| abstract_inverted_index.distresses, | 41 |
| abstract_inverted_index.efficiency, | 9 |
| abstract_inverted_index.frameworks. | 45 |
| abstract_inverted_index.geolocation | 36 |
| abstract_inverted_index.inaccuracy. | 22 |
| abstract_inverted_index.integrating | 133 |
| abstract_inverted_index.maintenance | 95, 147 |
| abstract_inverted_index.management. | 126 |
| abstract_inverted_index.operations. | 96 |
| abstract_inverted_index.significant | 47 |
| abstract_inverted_index.traditional | 11 |
| abstract_inverted_index.utilization | 27 |
| abstract_inverted_index.advancements | 144 |
| abstract_inverted_index.contribution | 48 |
| abstract_inverted_index.demonstrates | 107 |
| abstract_inverted_index.inspections, | 16, 119 |
| abstract_inverted_index.intelligence | 138 |
| abstract_inverted_index.specifically | 60 |
| abstract_inverted_index.environmental | 81 |
| abstract_inverted_index.substantially | 92 |
| abstract_inverted_index.technologies, | 139 |
| abstract_inverted_index.infrastructure | 125 |
| abstract_inverted_index.deep‐learning | 44 |
| abstract_inverted_index.cost‐effective | 89 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6100000143051147 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.89016018 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |