Matches in SemOpenAlex for { <https://semopenalex.org/work/W4298110856> ?p ?o ?g. }
- W4298110856 endingPage "036119812211238" @default.
- W4298110856 startingPage "036119812211238" @default.
- W4298110856 abstract "This paper has measured local traffic density under real driving conditions. The methodology employed is based on the traditional process of image recording. However, the conventional method has suffered from three typical limitations: long processing times, low vehicle detection performance, and inaccurate road length estimates. This study proposed two sequential approaches to resolve the three drawbacks. First, the you-only-look-once (YOLO) algorithm was used to shorten the computation time and enhance vehicle detection accuracy. Second, the vanishing point was identified during digital image processing to adjust the vertical and horizontal curves of roads, thereby calculating accurate road lengths. The proposed framework was applied to an arterial road in Seoul, South Korea. This approach was fast enough to measure local traffic density in real time. The performance of vehicle detection evaluated using a weighted mean average precision (mAP) was high at 88.73%. The accuracy of estimated road lengths evaluated using the Pearson correlation coefficient was also high at 0.97. Some co-benefits expected from the proposed method are also discussed." @default.
- W4298110856 created "2022-10-01" @default.
- W4298110856 creator A5042862178 @default.
- W4298110856 creator A5059211500 @default.
- W4298110856 creator A5063262053 @default.
- W4298110856 date "2022-09-30" @default.
- W4298110856 modified "2023-10-18" @default.
- W4298110856 title "Vehicle Detection Approach Adjusting Road Curves to Estimate Local Traffic Density under Real Driving Conditions" @default.
- W4298110856 cites W156483677 @default.
- W4298110856 cites W2031489346 @default.
- W4298110856 cites W2031601013 @default.
- W4298110856 cites W2039724563 @default.
- W4298110856 cites W2045290438 @default.
- W4298110856 cites W2053933273 @default.
- W4298110856 cites W2069619122 @default.
- W4298110856 cites W2070403512 @default.
- W4298110856 cites W2088173505 @default.
- W4298110856 cites W2090321073 @default.
- W4298110856 cites W2091533312 @default.
- W4298110856 cites W2095905764 @default.
- W4298110856 cites W2096257518 @default.
- W4298110856 cites W2111360370 @default.
- W4298110856 cites W2114376668 @default.
- W4298110856 cites W2116661744 @default.
- W4298110856 cites W2131076267 @default.
- W4298110856 cites W2136767008 @default.
- W4298110856 cites W2145023731 @default.
- W4298110856 cites W2170073444 @default.
- W4298110856 cites W2238185549 @default.
- W4298110856 cites W2337845997 @default.
- W4298110856 cites W2477868575 @default.
- W4298110856 cites W2565639579 @default.
- W4298110856 cites W2570343428 @default.
- W4298110856 cites W2574890738 @default.
- W4298110856 cites W2809686274 @default.
- W4298110856 cites W2811120218 @default.
- W4298110856 cites W2889035418 @default.
- W4298110856 cites W2900860600 @default.
- W4298110856 cites W2904734972 @default.
- W4298110856 cites W2913230672 @default.
- W4298110856 cites W2943134116 @default.
- W4298110856 cites W2955263881 @default.
- W4298110856 cites W2963037989 @default.
- W4298110856 cites W2963351448 @default.
- W4298110856 cites W2972167550 @default.
- W4298110856 cites W3106250896 @default.
- W4298110856 cites W3107727158 @default.
- W4298110856 cites W3107867277 @default.
- W4298110856 cites W3120363534 @default.
- W4298110856 cites W3158973191 @default.
- W4298110856 cites W3170818146 @default.
- W4298110856 cites W3180134609 @default.
- W4298110856 cites W3215073785 @default.
- W4298110856 cites W639708223 @default.
- W4298110856 cites W2913264686 @default.
- W4298110856 doi "https://doi.org/10.1177/03611981221123809" @default.
- W4298110856 hasPublicationYear "2022" @default.
- W4298110856 type Work @default.
- W4298110856 citedByCount "1" @default.
- W4298110856 countsByYear W42981108562023 @default.
- W4298110856 crossrefType "journal-article" @default.
- W4298110856 hasAuthorship W4298110856A5042862178 @default.
- W4298110856 hasAuthorship W4298110856A5059211500 @default.
- W4298110856 hasAuthorship W4298110856A5063262053 @default.
- W4298110856 hasConcept C111919701 @default.
- W4298110856 hasConcept C11413529 @default.
- W4298110856 hasConcept C115961682 @default.
- W4298110856 hasConcept C119857082 @default.
- W4298110856 hasConcept C124101348 @default.
- W4298110856 hasConcept C127413603 @default.
- W4298110856 hasConcept C154945302 @default.
- W4298110856 hasConcept C22212356 @default.
- W4298110856 hasConcept C2524010 @default.
- W4298110856 hasConcept C2780009758 @default.
- W4298110856 hasConcept C2780092901 @default.
- W4298110856 hasConcept C28719098 @default.
- W4298110856 hasConcept C2985695025 @default.
- W4298110856 hasConcept C33923547 @default.
- W4298110856 hasConcept C41008148 @default.
- W4298110856 hasConcept C44154836 @default.
- W4298110856 hasConcept C45374587 @default.
- W4298110856 hasConcept C9417928 @default.
- W4298110856 hasConcept C98045186 @default.
- W4298110856 hasConceptScore W4298110856C111919701 @default.
- W4298110856 hasConceptScore W4298110856C11413529 @default.
- W4298110856 hasConceptScore W4298110856C115961682 @default.
- W4298110856 hasConceptScore W4298110856C119857082 @default.
- W4298110856 hasConceptScore W4298110856C124101348 @default.
- W4298110856 hasConceptScore W4298110856C127413603 @default.
- W4298110856 hasConceptScore W4298110856C154945302 @default.
- W4298110856 hasConceptScore W4298110856C22212356 @default.
- W4298110856 hasConceptScore W4298110856C2524010 @default.
- W4298110856 hasConceptScore W4298110856C2780009758 @default.
- W4298110856 hasConceptScore W4298110856C2780092901 @default.
- W4298110856 hasConceptScore W4298110856C28719098 @default.
- W4298110856 hasConceptScore W4298110856C2985695025 @default.
- W4298110856 hasConceptScore W4298110856C33923547 @default.
- W4298110856 hasConceptScore W4298110856C41008148 @default.