Road Severity Distance Calculation Technique Using Deep Learning Predictions in 3-D Space Article Swipe
Asanka G. Perera
,
Brijesh Verma
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/access.2022.3185997
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/access.2022.3185997
Some roadside objects pose a significant danger to pedestrians and vehicles when they are too close to the road. A few examples are trees, poles and fences. Their proximity to the road can change over time due to natural conditions or human activities. Early detection of severe roadside conditions can help avoid accidents and save lives. However, detecting the roadside severity objects requires many resources and new techniques due to the size and complexity of the road network. Deep learning and image processing techniques can be leveraged to address this requirement and build an automatic roadside severity detection system. In this work, we propose a novel roadside attribute and distance calculation technique that extends our previous work in this area (lane-line method). The past work depended on the detected lane-line widths to calculate the distances. This method made mistakes in the presence of challenging road conditions and misclassifications. Here, we propose to combine camera configuration data with a neural network detector to develop a distance vs pixel model for reliable road severity distance calculation. We use camera metadata to transform the 2D image data predicted by the deep neural network into a 3D space. The improved model was tested with a real-world dataset. Compared to the lane-line method, the new combined model reported 36% and 37.5% accuracy improvements in right and left-hand side distances, respectively.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2022.3185997
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/09805747.pdf
- OA Status
- gold
- Cited By
- 4
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285289410
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285289410Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2022.3185997Digital Object Identifier
- Title
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Road Severity Distance Calculation Technique Using Deep Learning Predictions in 3-D SpaceWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
-
Asanka G. Perera, Brijesh VermaList of authors in order
- Landing page
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https://doi.org/10.1109/access.2022.3185997Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/09805747.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/09805747.pdfDirect OA link when available
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Computer science, Artificial intelligence, Deep learning, Line (geometry), Artificial neural network, Metadata, Computer vision, Convolutional neural network, Pixel, Detector, Pattern recognition (psychology), Mathematics, Telecommunications, Geometry, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2024: 3, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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20Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Deep | 78 |
| abstract_inverted_index.Some | 0 |
| abstract_inverted_index.This | 135 |
| abstract_inverted_index.area | 119 |
| abstract_inverted_index.data | 155, 183 |
| abstract_inverted_index.deep | 187 |
| abstract_inverted_index.help | 50 |
| abstract_inverted_index.into | 190 |
| abstract_inverted_index.made | 137 |
| abstract_inverted_index.many | 63 |
| abstract_inverted_index.over | 34 |
| abstract_inverted_index.past | 123 |
| abstract_inverted_index.pose | 3 |
| abstract_inverted_index.road | 31, 76, 144, 170 |
| abstract_inverted_index.save | 54 |
| abstract_inverted_index.side | 222 |
| abstract_inverted_index.size | 71 |
| abstract_inverted_index.that | 112 |
| abstract_inverted_index.they | 12 |
| abstract_inverted_index.this | 89, 100, 118 |
| abstract_inverted_index.time | 35 |
| abstract_inverted_index.when | 11 |
| abstract_inverted_index.with | 156, 199 |
| abstract_inverted_index.work | 116, 124 |
| abstract_inverted_index.Early | 43 |
| abstract_inverted_index.Here, | 148 |
| abstract_inverted_index.Their | 27 |
| abstract_inverted_index.avoid | 51 |
| abstract_inverted_index.build | 92 |
| abstract_inverted_index.close | 15 |
| abstract_inverted_index.human | 41 |
| abstract_inverted_index.image | 81, 182 |
| abstract_inverted_index.model | 167, 196, 211 |
| abstract_inverted_index.novel | 105 |
| abstract_inverted_index.pixel | 166 |
| abstract_inverted_index.poles | 24 |
| abstract_inverted_index.right | 219 |
| abstract_inverted_index.road. | 18 |
| abstract_inverted_index.work, | 101 |
| abstract_inverted_index.camera | 153, 176 |
| abstract_inverted_index.change | 33 |
| abstract_inverted_index.danger | 6 |
| abstract_inverted_index.lives. | 55 |
| abstract_inverted_index.method | 136 |
| abstract_inverted_index.neural | 158, 188 |
| abstract_inverted_index.severe | 46 |
| abstract_inverted_index.space. | 193 |
| abstract_inverted_index.tested | 198 |
| abstract_inverted_index.trees, | 23 |
| abstract_inverted_index.widths | 130 |
| abstract_inverted_index.address | 88 |
| abstract_inverted_index.combine | 152 |
| abstract_inverted_index.develop | 162 |
| abstract_inverted_index.extends | 113 |
| abstract_inverted_index.fences. | 26 |
| abstract_inverted_index.method, | 207 |
| abstract_inverted_index.natural | 38 |
| abstract_inverted_index.network | 159, 189 |
| abstract_inverted_index.objects | 2, 61 |
| abstract_inverted_index.propose | 103, 150 |
| abstract_inverted_index.system. | 98 |
| abstract_inverted_index.Compared | 203 |
| abstract_inverted_index.However, | 56 |
| abstract_inverted_index.accuracy | 216 |
| abstract_inverted_index.combined | 210 |
| abstract_inverted_index.dataset. | 202 |
| abstract_inverted_index.depended | 125 |
| abstract_inverted_index.detected | 128 |
| abstract_inverted_index.detector | 160 |
| abstract_inverted_index.distance | 109, 164, 172 |
| abstract_inverted_index.examples | 21 |
| abstract_inverted_index.improved | 195 |
| abstract_inverted_index.learning | 79 |
| abstract_inverted_index.metadata | 177 |
| abstract_inverted_index.method). | 121 |
| abstract_inverted_index.mistakes | 138 |
| abstract_inverted_index.network. | 77 |
| abstract_inverted_index.presence | 141 |
| abstract_inverted_index.previous | 115 |
| abstract_inverted_index.reliable | 169 |
| abstract_inverted_index.reported | 212 |
| abstract_inverted_index.requires | 62 |
| abstract_inverted_index.roadside | 1, 47, 59, 95, 106 |
| abstract_inverted_index.severity | 60, 96, 171 |
| abstract_inverted_index.vehicles | 10 |
| abstract_inverted_index.accidents | 52 |
| abstract_inverted_index.attribute | 107 |
| abstract_inverted_index.automatic | 94 |
| abstract_inverted_index.calculate | 132 |
| abstract_inverted_index.detecting | 57 |
| abstract_inverted_index.detection | 44, 97 |
| abstract_inverted_index.lane-line | 129, 206 |
| abstract_inverted_index.left-hand | 221 |
| abstract_inverted_index.leveraged | 86 |
| abstract_inverted_index.predicted | 184 |
| abstract_inverted_index.proximity | 28 |
| abstract_inverted_index.resources | 64 |
| abstract_inverted_index.technique | 111 |
| abstract_inverted_index.transform | 179 |
| abstract_inverted_index.(lane-line | 120 |
| abstract_inverted_index.36% | 213 |
| abstract_inverted_index.complexity | 73 |
| abstract_inverted_index.conditions | 39, 48, 145 |
| abstract_inverted_index.distances, | 223 |
| abstract_inverted_index.distances. | 134 |
| abstract_inverted_index.processing | 82 |
| abstract_inverted_index.real-world | 201 |
| abstract_inverted_index.techniques | 67, 83 |
| abstract_inverted_index.activities. | 42 |
| abstract_inverted_index.calculation | 110 |
| abstract_inverted_index.challenging | 143 |
| abstract_inverted_index.pedestrians | 8 |
| abstract_inverted_index.requirement | 90 |
| abstract_inverted_index.significant | 5 |
| abstract_inverted_index.37.5% | 215 |
| abstract_inverted_index.calculation. | 173 |
| abstract_inverted_index.improvements | 217 |
| abstract_inverted_index.configuration | 154 |
| abstract_inverted_index.respectively. | 224 |
| abstract_inverted_index.misclassifications. | 147 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.5449194 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |