Robust Road Sign Feature Extraction Through Data Curation and Multi-Task Learning for Global Map Creation Article Swipe
Godson Michael D’silva
,
Vinayak Ashok Bharadi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.14419/enqwz712
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.14419/enqwz712
This research presents a comprehensive approach to enhancing road sign feature extraction for global map creation through strategic improvements in data quality, feature learning, and network architecture. Designed to address core challenges in HERE Technologies' map creation pipeline (US patent PAN: 18/988231), our approach significantly improves the Stage 1 component of their patented three-stage framework by replacing the previous YOLOv7-based implementation with a more robust and effective solution. The methodology centers on three key innovations: (1) an intelligent data curation and filtering strategy that reduces annotation noise by 37% and improves overall data quality without extensive manual re-annotation; (2) novel self-supervised pretext tasks that develop rich feature representations of road sign characteristics such as color, shape, and contextual positioning; and (3) a multi-headed network architecture that preserves geometric understanding while enabling simultaneous optimization of detection, segmentation, and classification tasks. These innovations collectively address critical map creation challenges, including domain divergence between different imagery sources, class imbalance across sign types, data scarcity for rare classes, and noisy training samples. Evaluation metrics demonstrate exceptional improvements, with the enhanced system achieving 92% precision, 93% [email protected] for detection, and processing inputs 64.29% faster than the previous implementation while simultaneously performing multiple tasks. The approach significantly improves performance in challenging scenarios, with a 53% improvement in adverse lighting conditions and 31% higher accuracy in poor weather. By focusing on fundamental improvements in data quality, feature representation, and architectural design rather than simply adopting newer base models, this work establishes a foundation for more efficient and accurate feature extraction that enables faster global expansion of map coverage without sacrificing quality.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14419/enqwz712
- https://www.sciencepubco.com/index.php/IJBAS/article/download/34624/18631
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413128664
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413128664Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.14419/enqwz712Digital Object Identifier
- Title
-
Robust Road Sign Feature Extraction Through Data Curation and Multi-Task Learning for Global Map CreationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-12Full publication date if available
- Authors
-
Godson Michael D’silva, Vinayak Ashok BharadiList of authors in order
- Landing page
-
https://doi.org/10.14419/enqwz712Publisher landing page
- PDF URL
-
https://www.sciencepubco.com/index.php/IJBAS/article/download/34624/18631Direct 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://www.sciencepubco.com/index.php/IJBAS/article/download/34624/18631Direct OA link when available
- Concepts
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Sign (mathematics), Computer science, Task (project management), Feature (linguistics), Artificial intelligence, Road map, Feature extraction, Cartography, Geography, Engineering, Mathematics, Mathematical analysis, Linguistics, Systems engineering, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.inputs | 186 |
| abstract_inverted_index.manual | 96 |
| abstract_inverted_index.patent | 39 |
| abstract_inverted_index.rather | 234 |
| abstract_inverted_index.robust | 64 |
| abstract_inverted_index.shape, | 115 |
| abstract_inverted_index.simply | 236 |
| abstract_inverted_index.system | 176 |
| abstract_inverted_index.tasks. | 138, 197 |
| abstract_inverted_index.types, | 158 |
| abstract_inverted_index.map | 144 |
| abstract_inverted_index.address | 29, 142 |
| abstract_inverted_index.adverse | 211 |
| abstract_inverted_index.between | 150 |
| abstract_inverted_index.centers | 70 |
| abstract_inverted_index.develop | 104 |
| abstract_inverted_index.enables | 254 |
| abstract_inverted_index.feature | 10, 22, 106, 229, 251 |
| abstract_inverted_index.imagery | 152 |
| [email protected] | 181 |
| abstract_inverted_index.metrics | 169 |
| abstract_inverted_index.models, | 240 |
| abstract_inverted_index.network | 25, 123 |
| abstract_inverted_index.overall | 91 |
| abstract_inverted_index.pretext | 101 |
| abstract_inverted_index.quality | 93 |
| abstract_inverted_index.through | 16 |
| abstract_inverted_index.without | 94, 261 |
| abstract_inverted_index.data | 159 |
| abstract_inverted_index.Designed | 27 |
| abstract_inverted_index.accuracy | 217 |
| abstract_inverted_index.accurate | 250 |
| abstract_inverted_index.approach | 5, 43, 199 |
| abstract_inverted_index.classes, | 163 |
| abstract_inverted_index.coverage | 260 |
| abstract_inverted_index.creation | 15, 36, 145 |
| abstract_inverted_index.critical | 143 |
| abstract_inverted_index.curation | 79 |
| abstract_inverted_index.enabling | 130 |
| abstract_inverted_index.improves | 45, 90, 201 |
| abstract_inverted_index.lighting | 212 |
| abstract_inverted_index.multiple | 196 |
| abstract_inverted_index.patented | 52 |
| abstract_inverted_index.pipeline | 37 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.previous | 58 |
| abstract_inverted_index.quality, | 21, 228 |
| abstract_inverted_index.research | 1 |
| abstract_inverted_index.samples. | 167 |
| abstract_inverted_index.scarcity | 160 |
| abstract_inverted_index.sources, | 153 |
| abstract_inverted_index.strategy | 82 |
| abstract_inverted_index.training | 166 |
| abstract_inverted_index.weather. | 220 |
| abstract_inverted_index.achieving | 177 |
| abstract_inverted_index.component | 49 |
| abstract_inverted_index.different | 151 |
| abstract_inverted_index.efficient | 248 |
| abstract_inverted_index.enhancing | 7 |
| abstract_inverted_index.expansion | 257 |
| abstract_inverted_index.extensive | 95 |
| abstract_inverted_index.filtering | 81 |
| abstract_inverted_index.framework | 54 |
| abstract_inverted_index.geometric | 127 |
| abstract_inverted_index.imbalance | 155 |
| abstract_inverted_index.including | 147 |
| abstract_inverted_index.learning, | 23 |
| abstract_inverted_index.preserves | 126 |
| abstract_inverted_index.replacing | 56 |
| abstract_inverted_index.solution. | 67 |
| abstract_inverted_index.faster | 255 |
| abstract_inverted_index.Evaluation | 168 |
| abstract_inverted_index.annotation | 85 |
| abstract_inverted_index.challenges | 31 |
| abstract_inverted_index.conditions | 213 |
| abstract_inverted_index.detection, | 134, 183 |
| abstract_inverted_index.divergence | 149 |
| abstract_inverted_index.extraction | 11, 252 |
| abstract_inverted_index.foundation | 245 |
| abstract_inverted_index.performing | 195 |
| abstract_inverted_index.precision, | 179 |
| abstract_inverted_index.processing | 185 |
| abstract_inverted_index.scenarios, | 205 |
| abstract_inverted_index.reduces | 84 |
| abstract_inverted_index.18/988231), | 41 |
| abstract_inverted_index.challenges, | 146 |
| abstract_inverted_index.demonstrate | 170 |
| abstract_inverted_index.establishes | 243 |
| abstract_inverted_index.exceptional | 171 |
| abstract_inverted_index.fundamental | 224 |
| abstract_inverted_index.improvement | 209 |
| abstract_inverted_index.innovations | 140 |
| abstract_inverted_index.intelligent | 77 |
| abstract_inverted_index.methodology | 69 |
| abstract_inverted_index.performance | 202 |
| abstract_inverted_index.quality. | 263 |
| abstract_inverted_index.sacrificing | 262 |
| abstract_inverted_index.three-stage | 53 |
| abstract_inverted_index.adopting | 237 |
| abstract_inverted_index.enhanced | 175 |
| abstract_inverted_index.focusing | 222 |
| abstract_inverted_index.previous | 191 |
| abstract_inverted_index.YOLOv7-based | 59 |
| abstract_inverted_index.architecture | 124 |
| abstract_inverted_index.collectively | 141 |
| abstract_inverted_index.improvements | 18, 225 |
| abstract_inverted_index.innovations: | 74 |
| abstract_inverted_index.multi-headed | 122 |
| abstract_inverted_index.optimization | 132 |
| abstract_inverted_index.positioning; | 118 |
| abstract_inverted_index.effective | 66 |
| abstract_inverted_index.strategic | 17 |
| abstract_inverted_index.architectural | 232 |
| abstract_inverted_index.architecture. | 26 |
| abstract_inverted_index.comprehensive | 4 |
| abstract_inverted_index.improvements, | 172 |
| abstract_inverted_index.segmentation, | 135 |
| abstract_inverted_index.significantly | 44, 200 |
| abstract_inverted_index.understanding | 128 |
| abstract_inverted_index.contextual | 117 |
| abstract_inverted_index.classification | 137 |
| abstract_inverted_index.implementation | 60, 192 |
| abstract_inverted_index.re-annotation; | 97 |
| abstract_inverted_index.simultaneously | 194 |
| abstract_inverted_index.challenging | 204 |
| abstract_inverted_index.characteristics | 111 |
| abstract_inverted_index.representation, | 230 |
| abstract_inverted_index.representations | 107 |
| abstract_inverted_index.simultaneous | 131 |
| abstract_inverted_index.Technologies' | 34 |
| abstract_inverted_index.self-supervised | 100 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.42506938 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |