Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Hyo Jong | - |
dc.contributor.author | Ullah, Ihsan | - |
dc.contributor.author | Wan, Weiguo | - |
dc.contributor.author | Gao, Yongbin | - |
dc.contributor.author | Fang, Zhijun | - |
dc.date.accessioned | 2019-04-10T08:11:15Z | - |
dc.date.available | 2019-04-10T08:11:15Z | - |
dc.date.created | 2019-03-15 | - |
dc.date.issued | 2019-03 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/9745 | - |
dc.description.abstract | Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications. | - |
dc.language | English | - |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | - |
dc.title | Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/s19050982 | - |
dc.identifier.scopusid | 2-s2.0-85062434852 | - |
dc.identifier.bibliographicCitation | Sensors, v.19, no.5 | - |
dc.description.isOpenAccess | TRUE | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | residual SqueezeNet | - |
dc.subject.keywordAuthor | vehicle make recognition | - |
dc.subject.keywordPlus | REPRESENTATION | - |
dc.subject.keywordPlus | SURF | - |
dc.citation.number | 5 | - |
dc.citation.title | Sensors | - |
dc.citation.volume | 19 | - |