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dc.contributor.authorLin, Guan-Tingen_US
dc.contributor.authorSantoso, Patrisia Sherrylen_US
dc.contributor.authorLin, Che-Tsungen_US
dc.contributor.authorTsai, Chia-Chien_US
dc.contributor.authorGuo, Jiun-Inen_US
dc.date.accessioned2018-08-21T05:57:02Z-
dc.date.available2018-08-21T05:57:02Z-
dc.date.issued2017-01-01en_US
dc.identifier.issn2309-9402en_US
dc.identifier.urihttp://hdl.handle.net/11536/146965-
dc.description.abstractIn this paper, we introduce Boost-CNN, a robust stop-line detector that can detect objects (stop line) with competitive tradeoff between speed and accuracy. Boost-CNN consists of an AdaBoost classifier and a CNN. The former is our region proposal generator and it is further combined with the later to be a stop-line detector. In addition, an automatic hard mining method is proposed to reduce the number of false alarm. Our proposed detector achieves 91.5% in accuracy and has 100 FPS performance in test time (performed on NVITAA DIGITS DevBox and Titan X GPU).en_US
dc.language.isoen_USen_US
dc.titleStop Line Detection and Distance Measurement for Road Intersection based on. Deep Learning Neural Networken_US
dc.typeProceedings Paperen_US
dc.identifier.journal2017 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC 2017)en_US
dc.citation.spage692en_US
dc.citation.epage695en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.identifier.wosnumberWOS:000425879400128en_US
Appears in Collections:Conferences Paper