Vehicle Orientation Detection Using CNN
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nguyen Huu Thang | - |
dc.contributor.author | 김재민 | - |
dc.date.accessioned | 2022-01-20T05:42:23Z | - |
dc.date.available | 2022-01-20T05:42:23Z | - |
dc.date.created | 2022-01-20 | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1226-7244 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/24443 | - |
dc.description.abstract | Vehicle orientation detection is a challenging task because the orientations of vehicles can vary in a wide rangein captured images. The existing methods for oriented vehicle detection require too much computation time to beapplied to a real-time system. We propose Rotate YOLO, which has a set of anchor boxes with multiple scales,ratios, and angles to predict bounding boxes. For estimating the orientation angle, we applied angle-related IoUwith CIoU loss to solve the underivable problem from the calculation of SkewIoU. Evaluation results on threepublic datasets DLR Munich, VEDAI and UCAS-AOD demonstrate the efficiency of our approach | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | 한국전기전자학회 | - |
dc.title | Vehicle Orientation Detection Using CNN | - |
dc.title.alternative | Vehicle Orientation Detection Using CNN | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 김재민 | - |
dc.identifier.bibliographicCitation | 전기전자학회논문지, v.25, no.4, pp.619 - 624 | - |
dc.relation.isPartOf | 전기전자학회논문지 | - |
dc.citation.title | 전기전자학회논문지 | - |
dc.citation.volume | 25 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 619 | - |
dc.citation.endPage | 624 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002798081 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Vehicle Orientation | - |
dc.subject.keywordAuthor | Vehicle Detection | - |
dc.subject.keywordAuthor | Real-Time | - |
dc.subject.keywordAuthor | Convolutional Neural Network(CNN) | - |
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