FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3174769937> ?p ?o ?g. }

• W3174769937 endingPage "483" @default.
• W3174769937 startingPage "468" @default.
• W3174769937 abstract "Adverse weather has long been recognized as one of the major causes of motor vehicle crashes due to its negative impact on visibility and road surface. Providing drivers with real-time weather information is therefore extremely important to ensure safe driving in adverse weather. However, identification of road weather and surface conditions is a challenging task because it requires the deployment of expensive weather stations and often needs manual identification and/or verification. Most of the Department of Transportations (DOTs) in the U.S. have installed roadside webcams mostly for operational awareness. This study leveraged these easily accessible data sources to develop affordable automatic road weather and surface condition detection systems. The developed detection models are focused on three weather conditions; clear, light snow, and heavy snow; as well as three surface conditions: dry, snowy, wet/slushy. Several pre-trained Convolutional Neural Network (CNN) models, including AlexNet, GoogLeNet, and ResNet18, were applied with proper modification via transfer learning to achieve the classification tasks. The best performance was achieved using ResNet18 architecture with an unprecedented overall detection accuracy of 97% for weather detection and 99% for surface condition detection. The proposed study has the potential to provide more accurate and consistent weather information in real-time that can be made readily available to be used by road users and other transportation agencies. The proposed models could also be used to generate temporal and spatial variations of adverse weather for proper optimization of maintenance vehicles’ route and time." @default.
• W3174769937 created "2021-07-05" @default.
• W3174769937 creator A5072928629 @default.
• W3174769937 creator A5079368285 @default.
• W3174769937 date "2022-09-01" @default.
• W3174769937 modified "2023-10-18" @default.
• W3174769937 title "Weather and surface condition detection based on road-side webcams: Application of pre-trained Convolutional Neural Network" @default.
• W3174769937 cites W2039051707 @default.
• W3174769937 cites W2061004358 @default.
• W3174769937 cites W2064791460 @default.
• W3174769937 cites W2156520753 @default.
• W3174769937 cites W2194775991 @default.
• W3174769937 cites W2499581503 @default.
• W3174769937 cites W2795467836 @default.
• W3174769937 cites W2803974577 @default.
• W3174769937 cites W2806322885 @default.
• W3174769937 cites W2902878200 @default.
• W3174769937 cites W2905931162 @default.
• W3174769937 cites W2919600643 @default.
• W3174769937 cites W2938471138 @default.
• W3174769937 cites W2950682596 @default.
• W3174769937 cites W2953890940 @default.
• W3174769937 cites W2963856336 @default.
• W3174769937 cites W3006450303 @default.
• W3174769937 cites W3016070136 @default.
• W3174769937 cites W3017644243 @default.
• W3174769937 cites W3025199363 @default.
• W3174769937 cites W3025452776 @default.
• W3174769937 cites W3080886026 @default.
• W3174769937 cites W3195809468 @default.
• W3174769937 doi "https://doi.org/10.1016/j.ijtst.2021.06.003" @default.
• W3174769937 hasPublicationYear "2022" @default.
• W3174769937 type Work @default.
• W3174769937 sameAs 3174769937 @default.
• W3174769937 citedByCount "13" @default.
• W3174769937 countsByYear W31747699372021 @default.
• W3174769937 countsByYear W31747699372022 @default.
• W3174769937 countsByYear W31747699372023 @default.
• W3174769937 crossrefType "journal-article" @default.
• W3174769937 hasAuthorship W3174769937A5072928629 @default.
• W3174769937 hasAuthorship W3174769937A5079368285 @default.
• W3174769937 hasBestOaLocation W31747699371 @default.
• W3174769937 hasConcept C105339364 @default.
• W3174769937 hasConcept C108583219 @default.
• W3174769937 hasConcept C111919701 @default.
• W3174769937 hasConcept C116834253 @default.
• W3174769937 hasConcept C123403432 @default.
• W3174769937 hasConcept C127413603 @default.
• W3174769937 hasConcept C132651083 @default.
• W3174769937 hasConcept C147176958 @default.
• W3174769937 hasConcept C153294291 @default.
• W3174769937 hasConcept C154945302 @default.
• W3174769937 hasConcept C171146098 @default.
• W3174769937 hasConcept C18903297 @default.
• W3174769937 hasConcept C197046000 @default.
• W3174769937 hasConcept C205537798 @default.
• W3174769937 hasConcept C205649164 @default.
• W3174769937 hasConcept C21001229 @default.
• W3174769937 hasConcept C22212356 @default.
• W3174769937 hasConcept C2780042925 @default.
• W3174769937 hasConcept C2781290007 @default.
• W3174769937 hasConcept C2992147540 @default.
• W3174769937 hasConcept C39432304 @default.
• W3174769937 hasConcept C41008148 @default.
• W3174769937 hasConcept C52121051 @default.
• W3174769937 hasConcept C59822182 @default.
• W3174769937 hasConcept C81363708 @default.
• W3174769937 hasConcept C86803240 @default.
• W3174769937 hasConceptScore W3174769937C105339364 @default.
• W3174769937 hasConceptScore W3174769937C108583219 @default.
• W3174769937 hasConceptScore W3174769937C111919701 @default.
• W3174769937 hasConceptScore W3174769937C116834253 @default.
• W3174769937 hasConceptScore W3174769937C123403432 @default.
• W3174769937 hasConceptScore W3174769937C127413603 @default.
• W3174769937 hasConceptScore W3174769937C132651083 @default.
• W3174769937 hasConceptScore W3174769937C147176958 @default.
• W3174769937 hasConceptScore W3174769937C153294291 @default.
• W3174769937 hasConceptScore W3174769937C154945302 @default.
• W3174769937 hasConceptScore W3174769937C171146098 @default.
• W3174769937 hasConceptScore W3174769937C18903297 @default.
• W3174769937 hasConceptScore W3174769937C197046000 @default.
• W3174769937 hasConceptScore W3174769937C205537798 @default.
• W3174769937 hasConceptScore W3174769937C205649164 @default.
• W3174769937 hasConceptScore W3174769937C21001229 @default.
• W3174769937 hasConceptScore W3174769937C22212356 @default.
• W3174769937 hasConceptScore W3174769937C2780042925 @default.
• W3174769937 hasConceptScore W3174769937C2781290007 @default.
• W3174769937 hasConceptScore W3174769937C2992147540 @default.
• W3174769937 hasConceptScore W3174769937C39432304 @default.
• W3174769937 hasConceptScore W3174769937C41008148 @default.
• W3174769937 hasConceptScore W3174769937C52121051 @default.
• W3174769937 hasConceptScore W3174769937C59822182 @default.
• W3174769937 hasConceptScore W3174769937C81363708 @default.
• W3174769937 hasConceptScore W3174769937C86803240 @default.
• W3174769937 hasFunder F4320315307 @default.
• W3174769937 hasIssue "3" @default.
• W3174769937 hasLocation W31747699371 @default.
• W3174769937 hasOpenAccess W3174769937 @default.

• next