Convolution Neural Network with Selective Multi-Stage Feature Fusion: Case Study on Vehicle Rear Detection
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Won-Jae | - |
dc.contributor.author | Kim, Dong W. | - |
dc.contributor.author | Kang, Tae-Koo | - |
dc.contributor.author | Lim, Myo-Taeg | - |
dc.date.accessioned | 2021-09-02T02:30:23Z | - |
dc.date.available | 2021-09-02T02:30:23Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2018-12 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/71365 | - |
dc.description.abstract | Vision-based vehicle detection is the most basic and important technology in advanced driver assistance systems. In this paper, we propose a vehicle detection framework using selective multi-stage features in convolutional neural networks (CNNs) to improve vehicle detection performance. A 10-layer CNN model was designed and visualization techniques were used to selectively extract features from the activation feature map, called selective multi-stage features. The proposed features contain characteristic vehicle image information and are more robust than traditional features against noise. We trained the AdaBoost algorithm using these features to implement a vehicle detector. The experimental results verified that the proposed vehicle detection framework exhibited better performance than previous frameworks. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | Convolution Neural Network with Selective Multi-Stage Feature Fusion: Case Study on Vehicle Rear Detection | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lim, Myo-Taeg | - |
dc.identifier.doi | 10.3390/app8122468 | - |
dc.identifier.scopusid | 2-s2.0-85057842574 | - |
dc.identifier.wosid | 000455145000136 | - |
dc.identifier.bibliographicCitation | APPLIED SCIENCES-BASEL, v.8, no.12 | - |
dc.relation.isPartOf | APPLIED SCIENCES-BASEL | - |
dc.citation.title | APPLIED SCIENCES-BASEL | - |
dc.citation.volume | 8 | - |
dc.citation.number | 12 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.subject.keywordAuthor | vehicle detection | - |
dc.subject.keywordAuthor | feature extraction | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.subject.keywordAuthor | AdaBoost | - |
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