Assessing Precision and Dependability of Reconstructed Three-Dimensional Modeling for Vehicles at Crash Scenes using Unmanned Aircraft System Article Swipe
Akmal Jauhari Jalal
,
Mohd Farid Mohd Ariff
,
Ahmad Firdaus Razali
,
Razak Wong Chen Keng
,
Mohamad Ariff Wook
,
Mohamad Ikhwan Idris
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.11113/jagst.v3n2.76
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.11113/jagst.v3n2.76
This study focuses on the accuracy assessment of 3D reconstructions of crime scenes using Unmanned Aircraft Systems (UAS) and Terrestrial Laser Scanners (TLS) data for forensic crash investigation. Forensic crash investigation involves meticulously analyzing physical evidence, vehicles, and human factors in road collisions to determine the sequence of events. Preserving the original state of the crash scene before cleaning is essential for accurate forensic analysis. However, this preservation process can disrupt normal activities and demand considerable time. Geomatic technology, specifically UAS or drones, offers a potential solution for efficient and precise forensic mapping. The application of UAS technology enables swift data collection, leading to cost savings, enhanced safety, and data utilization. This study aims to assess the suitability of UAS techniques for forensic mapping, encompassing both relative and absolute accuracy. This research uses a UAS to rapidly and comprehensively capture evidence from a simulated crash site using predefined flight paths. The acquired image data is then processed utilizing Agisoft Metashape software, generating a detailed 3D model of the crash scene. This model can be enriched with annotations, measurements, and pertinent information. A comparative analysis is performed by preparing a table that contrasts the absolute and relative accuracy of UAS-collected data with that obtained from TLS, which serves as a benchmark. The results reveal that the UAS demonstrates a relative accuracy Root Mean Square Error (RMSE) of approximately ±4.1 cm compared to TLS. Concerning absolute precision, the UAS-produced RMSE values are determined as ±0.20719 for the X coordinate, ±0.164 for the Y coordinate, and ±0.001584 for the Z coordinate compared to GNSS data, which functions as the benchmark. The utilization of UAS technology offers a non-invasive measurement approach that eliminates direct physical contact between the operator and the documented object. This non-intrusive method ensures the preservation of the original scene characteristics and has shown its superiority over conventional approaches in managing crash scenes. Overall, this study underscores the potential of UAS technology in accurately reconstructing crime scenes for forensic investigation purposes.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.11113/jagst.v3n2.76
- https://jagst.utm.my/index.php/jagst/article/download/76/40
- OA Status
- diamond
- Cited By
- 1
- References
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386419805
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386419805Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.11113/jagst.v3n2.76Digital Object Identifier
- Title
-
Assessing Precision and Dependability of Reconstructed Three-Dimensional Modeling for Vehicles at Crash Scenes using Unmanned Aircraft SystemWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-30Full publication date if available
- Authors
-
Akmal Jauhari Jalal, Mohd Farid Mohd Ariff, Ahmad Firdaus Razali, Razak Wong Chen Keng, Mohamad Ariff Wook, Mohamad Ikhwan IdrisList of authors in order
- Landing page
-
https://doi.org/10.11113/jagst.v3n2.76Publisher landing page
- PDF URL
-
https://jagst.utm.my/index.php/jagst/article/download/76/40Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://jagst.utm.my/index.php/jagst/article/download/76/40Direct OA link when available
- Concepts
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Crash, Computer science, Mean squared error, Benchmark (surveying), Dependability, Artificial intelligence, Simulation, Statistics, Cartography, Mathematics, Software engineering, Geography, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
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11Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.The | 93, 150, 210, 267 |
| abstract_inverted_index.UAS | 80, 96, 119, 134, 215, 270, 319 |
| abstract_inverted_index.and | 18, 37, 73, 89, 108, 127, 137, 178, 194, 252, 285, 300 |
| abstract_inverted_index.are | 239 |
| abstract_inverted_index.can | 69, 172 |
| abstract_inverted_index.for | 24, 61, 87, 121, 243, 248, 254, 326 |
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| abstract_inverted_index.its | 303 |
| abstract_inverted_index.the | 4, 45, 50, 54, 116, 167, 192, 214, 235, 244, 249, 255, 265, 283, 286, 293, 296, 316 |
| abstract_inverted_index.GNSS | 260 |
| abstract_inverted_index.Mean | 221 |
| abstract_inverted_index.RMSE | 237 |
| abstract_inverted_index.Root | 220 |
| abstract_inverted_index.TLS, | 204 |
| abstract_inverted_index.TLS. | 231 |
| abstract_inverted_index.This | 0, 111, 130, 170, 289 |
| abstract_inverted_index.aims | 113 |
| abstract_inverted_index.both | 125 |
| abstract_inverted_index.cost | 104 |
| abstract_inverted_index.data | 23, 100, 109, 153, 199 |
| abstract_inverted_index.from | 141, 203 |
| abstract_inverted_index.over | 305 |
| abstract_inverted_index.road | 41 |
| abstract_inverted_index.site | 145 |
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| abstract_inverted_index.this | 66, 313 |
| abstract_inverted_index.uses | 132 |
| abstract_inverted_index.with | 175, 200 |
| abstract_inverted_index.(TLS) | 22 |
| abstract_inverted_index.(UAS) | 17 |
| abstract_inverted_index.Error | 223 |
| abstract_inverted_index.Laser | 20 |
| abstract_inverted_index.crash | 26, 29, 55, 144, 168, 310 |
| abstract_inverted_index.crime | 11, 324 |
| abstract_inverted_index.data, | 261 |
| abstract_inverted_index.human | 38 |
| abstract_inverted_index.image | 152 |
| abstract_inverted_index.model | 165, 171 |
| abstract_inverted_index.scene | 56, 298 |
| abstract_inverted_index.shown | 302 |
| abstract_inverted_index.state | 52 |
| abstract_inverted_index.study | 1, 112, 314 |
| abstract_inverted_index.swift | 99 |
| abstract_inverted_index.table | 189 |
| abstract_inverted_index.time. | 76 |
| abstract_inverted_index.using | 13, 146 |
| abstract_inverted_index.which | 205, 262 |
| abstract_inverted_index.±4.1 | 227 |
| abstract_inverted_index.(RMSE) | 224 |
| abstract_inverted_index.Square | 222 |
| abstract_inverted_index.assess | 115 |
| abstract_inverted_index.before | 57 |
| abstract_inverted_index.demand | 74 |
| abstract_inverted_index.direct | 279 |
| abstract_inverted_index.flight | 148 |
| abstract_inverted_index.method | 291 |
| abstract_inverted_index.normal | 71 |
| abstract_inverted_index.offers | 83, 272 |
| abstract_inverted_index.paths. | 149 |
| abstract_inverted_index.reveal | 212 |
| abstract_inverted_index.scene. | 169 |
| abstract_inverted_index.scenes | 12, 325 |
| abstract_inverted_index.serves | 206 |
| abstract_inverted_index.values | 238 |
| abstract_inverted_index.Agisoft | 158 |
| abstract_inverted_index.Systems | 16 |
| abstract_inverted_index.between | 282 |
| abstract_inverted_index.capture | 139 |
| abstract_inverted_index.contact | 281 |
| abstract_inverted_index.disrupt | 70 |
| abstract_inverted_index.drones, | 82 |
| abstract_inverted_index.enables | 98 |
| abstract_inverted_index.ensures | 292 |
| abstract_inverted_index.events. | 48 |
| abstract_inverted_index.factors | 39 |
| abstract_inverted_index.focuses | 2 |
| abstract_inverted_index.leading | 102 |
| abstract_inverted_index.object. | 288 |
| abstract_inverted_index.precise | 90 |
| abstract_inverted_index.process | 68 |
| abstract_inverted_index.rapidly | 136 |
| abstract_inverted_index.results | 211 |
| abstract_inverted_index.safety, | 107 |
| abstract_inverted_index.scenes. | 311 |
| abstract_inverted_index.±0.164 | 247 |
| abstract_inverted_index.Aircraft | 15 |
| abstract_inverted_index.Forensic | 28 |
| abstract_inverted_index.Geomatic | 77 |
| abstract_inverted_index.However, | 65 |
| abstract_inverted_index.Overall, | 312 |
| abstract_inverted_index.Scanners | 21 |
| abstract_inverted_index.Unmanned | 14 |
| abstract_inverted_index.absolute | 128, 193, 233 |
| abstract_inverted_index.accuracy | 5, 196, 219 |
| abstract_inverted_index.accurate | 62 |
| abstract_inverted_index.acquired | 151 |
| abstract_inverted_index.analysis | 183 |
| abstract_inverted_index.approach | 276 |
| abstract_inverted_index.cleaning | 58 |
| abstract_inverted_index.compared | 229, 258 |
| abstract_inverted_index.detailed | 163 |
| abstract_inverted_index.enhanced | 106 |
| abstract_inverted_index.enriched | 174 |
| abstract_inverted_index.evidence | 140 |
| abstract_inverted_index.forensic | 25, 63, 91, 122, 327 |
| abstract_inverted_index.involves | 31 |
| abstract_inverted_index.managing | 309 |
| abstract_inverted_index.mapping, | 123 |
| abstract_inverted_index.mapping. | 92 |
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| abstract_inverted_index.operator | 284 |
| abstract_inverted_index.original | 51, 297 |
| abstract_inverted_index.physical | 34, 280 |
| abstract_inverted_index.relative | 126, 195, 218 |
| abstract_inverted_index.research | 131 |
| abstract_inverted_index.savings, | 105 |
| abstract_inverted_index.sequence | 46 |
| abstract_inverted_index.solution | 86 |
| abstract_inverted_index.Metashape | 159 |
| abstract_inverted_index.accuracy. | 129 |
| abstract_inverted_index.analysis. | 64 |
| abstract_inverted_index.analyzing | 33 |
| abstract_inverted_index.contrasts | 191 |
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| abstract_inverted_index.essential | 60 |
| abstract_inverted_index.evidence, | 35 |
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| abstract_inverted_index.pertinent | 179 |
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| abstract_inverted_index.preparing | 187 |
| abstract_inverted_index.processed | 156 |
| abstract_inverted_index.purposes. | 329 |
| abstract_inverted_index.simulated | 143 |
| abstract_inverted_index.software, | 160 |
| abstract_inverted_index.utilizing | 157 |
| abstract_inverted_index.vehicles, | 36 |
| abstract_inverted_index.±0.20719 | 242 |
| abstract_inverted_index.Concerning | 232 |
| abstract_inverted_index.Preserving | 49 |
| abstract_inverted_index.accurately | 322 |
| abstract_inverted_index.activities | 72 |
| abstract_inverted_index.approaches | 307 |
| abstract_inverted_index.assessment | 6 |
| abstract_inverted_index.benchmark. | 209, 266 |
| abstract_inverted_index.collisions | 42 |
| abstract_inverted_index.coordinate | 257 |
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| abstract_inverted_index.documented | 287 |
| abstract_inverted_index.eliminates | 278 |
| abstract_inverted_index.generating | 161 |
| abstract_inverted_index.precision, | 234 |
| abstract_inverted_index.predefined | 147 |
| abstract_inverted_index.techniques | 120 |
| abstract_inverted_index.technology | 97, 271, 320 |
| abstract_inverted_index.±0.001584 | 253 |
| abstract_inverted_index.Terrestrial | 19 |
| abstract_inverted_index.application | 94 |
| abstract_inverted_index.collection, | 101 |
| abstract_inverted_index.comparative | 182 |
| abstract_inverted_index.coordinate, | 246, 251 |
| abstract_inverted_index.measurement | 275 |
| abstract_inverted_index.suitability | 117 |
| abstract_inverted_index.superiority | 304 |
| abstract_inverted_index.technology, | 78 |
| abstract_inverted_index.underscores | 315 |
| abstract_inverted_index.utilization | 268 |
| abstract_inverted_index.UAS-produced | 236 |
| abstract_inverted_index.annotations, | 176 |
| abstract_inverted_index.considerable | 75 |
| abstract_inverted_index.conventional | 306 |
| abstract_inverted_index.demonstrates | 216 |
| abstract_inverted_index.encompassing | 124 |
| abstract_inverted_index.information. | 180 |
| abstract_inverted_index.meticulously | 32 |
| abstract_inverted_index.non-invasive | 274 |
| abstract_inverted_index.preservation | 67, 294 |
| abstract_inverted_index.specifically | 79 |
| abstract_inverted_index.utilization. | 110 |
| abstract_inverted_index.UAS-collected | 198 |
| abstract_inverted_index.approximately | 226 |
| abstract_inverted_index.investigation | 30, 328 |
| abstract_inverted_index.measurements, | 177 |
| abstract_inverted_index.non-intrusive | 290 |
| abstract_inverted_index.investigation. | 27 |
| abstract_inverted_index.reconstructing | 323 |
| abstract_inverted_index.characteristics | 299 |
| abstract_inverted_index.comprehensively | 138 |
| abstract_inverted_index.reconstructions | 9 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.67885546 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |