Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lin, Guan-Ting | en_US |
dc.contributor.author | Santoso, Patrisia Sherryl | en_US |
dc.contributor.author | Lin, Che-Tsung | en_US |
dc.contributor.author | Tsai, Chia-Chi | en_US |
dc.contributor.author | Guo, Jiun-In | en_US |
dc.date.accessioned | 2018-08-21T05:57:02Z | - |
dc.date.available | 2018-08-21T05:57:02Z | - |
dc.date.issued | 2017-01-01 | en_US |
dc.identifier.issn | 2309-9402 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/146965 | - |
dc.description.abstract | In 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.iso | en_US | en_US |
dc.title | Stop Line Detection and Distance Measurement for Road Intersection based on. Deep Learning Neural Network | en_US |
dc.type | Proceedings Paper | en_US |
dc.identifier.journal | 2017 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC 2017) | en_US |
dc.citation.spage | 692 | en_US |
dc.citation.epage | 695 | en_US |
dc.contributor.department | 交大名義發表 | zh_TW |
dc.contributor.department | National Chiao Tung University | en_US |
dc.identifier.wosnumber | WOS:000425879400128 | en_US |
Appears in Collections: | Conferences Paper |