Deep binary classification via multi-resolution network and stochastic orthogonality for subcompact vehicle recognition
DC Field | Value | Language |
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dc.contributor.author | Shin J. | - |
dc.contributor.author | Koo B. | - |
dc.contributor.author | Kim Y. | - |
dc.contributor.author | Paik J. | - |
dc.date.available | 2020-06-17T01:20:17Z | - |
dc.date.issued | 2020-05 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.issn | 1424-3210 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/40712 | - |
dc.description.abstract | To encourage people to save energy, subcompact cars have several benefits of discount on parking or toll road charge. However, manual classification of the subcompact car is highly labor intensive. To solve this problem, automatic vehicle classification systems are good candidates. Since a general pattern-based classification technique can not successfully recognize the ambiguous features of a vehicle, we present a new multi-resolution convolutional neural network (CNN) and stochastic orthogonal learning method to train the network. We first extract the region of a bonnet in the vehicle image. Next, both extracted and input image are engaged to low and high resolution layers in the CNN model. The proposed network is then optimized based on stochastic orthogonality. We also built a novel subcompact vehicle dataset that will be open for a public use. Experimental results show that the proposed model outperforms state-of-the-art approaches in term of accuracy, which means that the proposed method can efficiently classify the ambiguous features between subcompact and non-subcompact vehicles. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI AG | - |
dc.title | Deep binary classification via multi-resolution network and stochastic orthogonality for subcompact vehicle recognition | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/s20092715 | - |
dc.identifier.bibliographicCitation | Sensors (Switzerland), v.20, no.9 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 000537106200279 | - |
dc.identifier.scopusid | 2-s2.0-85084514274 | - |
dc.citation.number | 9 | - |
dc.citation.title | Sensors (Switzerland) | - |
dc.citation.volume | 20 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | Multi resolution network | - |
dc.subject.keywordAuthor | Optimization | - |
dc.subject.keywordAuthor | Vehicle recognition | - |
dc.subject.keywordPlus | Convolutional neural networks | - |
dc.subject.keywordPlus | Image segmentation | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Toll highways | - |
dc.subject.keywordPlus | Vehicles | - |
dc.subject.keywordPlus | Automatic vehicles | - |
dc.subject.keywordPlus | Binary classification | - |
dc.subject.keywordPlus | Classification technique | - |
dc.subject.keywordPlus | General patterns | - |
dc.subject.keywordPlus | Learning methods | - |
dc.subject.keywordPlus | Manual classification | - |
dc.subject.keywordPlus | State-of-the-art approach | - |
dc.subject.keywordPlus | Vehicle recognition | - |
dc.subject.keywordPlus | Stochastic systems | - |
dc.subject.keywordPlus | article | - |
dc.subject.keywordPlus | binary classification | - |
dc.subject.keywordPlus | convolutional neural network | - |
dc.subject.keywordPlus | human | - |
dc.subject.keywordPlus | stochastic model | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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