Matches in SemOpenAlex for { <https://semopenalex.org/work/W2908176645> ?p ?o ?g. }
Showing items 1 to 54 of
54
with 100 items per page.
- W2908176645 endingPage "1910" @default.
- W2908176645 startingPage "1903" @default.
- W2908176645 abstract "Heavy vehicles develop technical snag and traffic jam on streets. Accidents between heavy vehicle and road users, for example, pedestrians often result in severe injuries of the weaker street users. The highway safety and traffic jams can be secured with detection of heavy and overloaded vehicles on the highway to facilitate light motor vehicles like cars, scooters. A model for heavy vehicle detection using fine-tuned based on deep learning is proposed to deal with entangled transportation scene. This model comprises two parts, vehicle detection model and vehicle fine-grained detection. This step provides data for the next classification model. Experiments show that vehicle’s make and model can be recognized from transportation images effectively by using our method. Experimental results demonstrate that the proposed detection system performs accurately with other simple and complex scenarios in detecting heavy vehicles in comparison with past vehicle detection systems." @default.
- W2908176645 created "2019-01-11" @default.
- W2908176645 creator A5035939973 @default.
- W2908176645 creator A5073615106 @default.
- W2908176645 date "2019-01-01" @default.
- W2908176645 modified "2023-09-26" @default.
- W2908176645 title "Heavy Vehicle Detection Using Fine-Tuned Deep Learning" @default.
- W2908176645 cites W1929903369 @default.
- W2908176645 cites W196211074 @default.
- W2908176645 cites W2026719822 @default.
- W2908176645 cites W2043616860 @default.
- W2908176645 cites W2072852087 @default.
- W2908176645 cites W2125085157 @default.
- W2908176645 cites W2144426297 @default.
- W2908176645 cites W2168296665 @default.
- W2908176645 cites W225438136 @default.
- W2908176645 cites W2491988996 @default.
- W2908176645 cites W2964036919 @default.
- W2908176645 cites W3098507037 @default.
- W2908176645 doi "https://doi.org/10.1007/978-3-030-00665-5_175" @default.
- W2908176645 hasPublicationYear "2019" @default.
- W2908176645 type Work @default.
- W2908176645 sameAs 2908176645 @default.
- W2908176645 citedByCount "0" @default.
- W2908176645 crossrefType "book-chapter" @default.
- W2908176645 hasAuthorship W2908176645A5035939973 @default.
- W2908176645 hasAuthorship W2908176645A5073615106 @default.
- W2908176645 hasConcept C108583219 @default.
- W2908176645 hasConcept C154945302 @default.
- W2908176645 hasConcept C39432304 @default.
- W2908176645 hasConcept C41008148 @default.
- W2908176645 hasConceptScore W2908176645C108583219 @default.
- W2908176645 hasConceptScore W2908176645C154945302 @default.
- W2908176645 hasConceptScore W2908176645C39432304 @default.
- W2908176645 hasConceptScore W2908176645C41008148 @default.
- W2908176645 hasLocation W29081766451 @default.
- W2908176645 hasOpenAccess W2908176645 @default.
- W2908176645 hasPrimaryLocation W29081766451 @default.
- W2908176645 hasRelatedWork W2126887587 @default.
- W2908176645 hasRelatedWork W2731899572 @default.
- W2908176645 hasRelatedWork W2899084033 @default.
- W2908176645 hasRelatedWork W2939353110 @default.
- W2908176645 hasRelatedWork W3009238340 @default.
- W2908176645 hasRelatedWork W3215138031 @default.
- W2908176645 hasRelatedWork W4312962853 @default.
- W2908176645 hasRelatedWork W4321369474 @default.
- W2908176645 hasRelatedWork W4327774331 @default.
- W2908176645 hasRelatedWork W4360585206 @default.
- W2908176645 isParatext "false" @default.
- W2908176645 isRetracted "false" @default.
- W2908176645 magId "2908176645" @default.
- W2908176645 workType "book-chapter" @default.