Matches in SemOpenAlex for { <https://semopenalex.org/work/W4379054048> ?p ?o ?g. }
- W4379054048 endingPage "481" @default.
- W4379054048 startingPage "461" @default.
- W4379054048 abstract "Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. This paper uses simulated data and transfer learning to build a robust real-world model for vehicle detection and classification from single-beam LiDAR of a roadside parking scenario. The paper presents a synthetically augmented transfer learning approach for LiDAR-based vehicle detection and the implementation of synthetic LiDAR data. A synthetic augmented transfer learning method was used to supplement the small real-world data set and allow the development of data-handling techniques. In addition, adding the synthetically augmented transfer learning method increases the robustness and overall accuracy of the model. Experiments show that the method can be used for fast deployment of the model for vehicle detection using a LIDAR sensor." @default.
- W4379054048 created "2023-06-02" @default.
- W4379054048 creator A5006800217 @default.
- W4379054048 creator A5036391277 @default.
- W4379054048 creator A5037724783 @default.
- W4379054048 creator A5051787059 @default.
- W4379054048 creator A5052403262 @default.
- W4379054048 creator A5061612771 @default.
- W4379054048 creator A5066707391 @default.
- W4379054048 creator A5084010930 @default.
- W4379054048 creator A5090161057 @default.
- W4379054048 creator A5092068889 @default.
- W4379054048 date "2023-06-01" @default.
- W4379054048 modified "2023-10-06" @default.
- W4379054048 title "A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods" @default.
- W4379054048 cites W1536680647 @default.
- W4379054048 cites W1980955748 @default.
- W4379054048 cites W2015112491 @default.
- W4379054048 cites W2048077401 @default.
- W4379054048 cites W2090860576 @default.
- W4379054048 cites W2106688579 @default.
- W4379054048 cites W2108598243 @default.
- W4379054048 cites W2113799686 @default.
- W4379054048 cites W2132140174 @default.
- W4379054048 cites W2165698076 @default.
- W4379054048 cites W2395579298 @default.
- W4379054048 cites W2555618208 @default.
- W4379054048 cites W2557728737 @default.
- W4379054048 cites W2570343428 @default.
- W4379054048 cites W2608369674 @default.
- W4379054048 cites W2763108738 @default.
- W4379054048 cites W2774434529 @default.
- W4379054048 cites W2801492038 @default.
- W4379054048 cites W2802344721 @default.
- W4379054048 cites W2805899829 @default.
- W4379054048 cites W2887280559 @default.
- W4379054048 cites W2897870845 @default.
- W4379054048 cites W2921271497 @default.
- W4379054048 cites W2921642046 @default.
- W4379054048 cites W2936093102 @default.
- W4379054048 cites W2944278099 @default.
- W4379054048 cites W2947742306 @default.
- W4379054048 cites W2951592577 @default.
- W4379054048 cites W2963037989 @default.
- W4379054048 cites W2963083779 @default.
- W4379054048 cites W2963201472 @default.
- W4379054048 cites W2966367600 @default.
- W4379054048 cites W2988868065 @default.
- W4379054048 cites W2990057671 @default.
- W4379054048 cites W3003348291 @default.
- W4379054048 cites W3107562115 @default.
- W4379054048 cites W3202375294 @default.
- W4379054048 cites W3216568609 @default.
- W4379054048 cites W4253489187 @default.
- W4379054048 cites W4255421341 @default.
- W4379054048 cites W4295832403 @default.
- W4379054048 doi "https://doi.org/10.3390/ai4020025" @default.
- W4379054048 hasPublicationYear "2023" @default.
- W4379054048 type Work @default.
- W4379054048 citedByCount "0" @default.
- W4379054048 crossrefType "journal-article" @default.
- W4379054048 hasAuthorship W4379054048A5006800217 @default.
- W4379054048 hasAuthorship W4379054048A5036391277 @default.
- W4379054048 hasAuthorship W4379054048A5037724783 @default.
- W4379054048 hasAuthorship W4379054048A5051787059 @default.
- W4379054048 hasAuthorship W4379054048A5052403262 @default.
- W4379054048 hasAuthorship W4379054048A5061612771 @default.
- W4379054048 hasAuthorship W4379054048A5066707391 @default.
- W4379054048 hasAuthorship W4379054048A5084010930 @default.
- W4379054048 hasAuthorship W4379054048A5090161057 @default.
- W4379054048 hasAuthorship W4379054048A5092068889 @default.
- W4379054048 hasBestOaLocation W43790540481 @default.
- W4379054048 hasConcept C104317684 @default.
- W4379054048 hasConcept C105339364 @default.
- W4379054048 hasConcept C111919701 @default.
- W4379054048 hasConcept C115051666 @default.
- W4379054048 hasConcept C127413603 @default.
- W4379054048 hasConcept C150899416 @default.
- W4379054048 hasConcept C154945302 @default.
- W4379054048 hasConcept C185592680 @default.
- W4379054048 hasConcept C188198153 @default.
- W4379054048 hasConcept C205649164 @default.
- W4379054048 hasConcept C41008148 @default.
- W4379054048 hasConcept C51399673 @default.
- W4379054048 hasConcept C55493867 @default.
- W4379054048 hasConcept C62649853 @default.
- W4379054048 hasConcept C63479239 @default.
- W4379054048 hasConcept C67186912 @default.
- W4379054048 hasConcept C76155785 @default.
- W4379054048 hasConcept C77088390 @default.
- W4379054048 hasConcept C78519656 @default.
- W4379054048 hasConceptScore W4379054048C104317684 @default.
- W4379054048 hasConceptScore W4379054048C105339364 @default.
- W4379054048 hasConceptScore W4379054048C111919701 @default.
- W4379054048 hasConceptScore W4379054048C115051666 @default.
- W4379054048 hasConceptScore W4379054048C127413603 @default.
- W4379054048 hasConceptScore W4379054048C150899416 @default.
- W4379054048 hasConceptScore W4379054048C154945302 @default.