cid ced70ce5bf7482b0678c0e7ea8ef796a2c4e9614e55caba2cdc5786075bca070 · original · via openalex
Title: Stop line detection and distance measurement for road intersection based on deep learning neural network Authors: Guan-Ting Lin; Patrisia Sherryl Santoso; Che-Tsung Lin; Chia–Chi Tsai; Jiun-In Guo Venue: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Year: 2017 Canonical: doi:10.1109/apsipa.2017.8282121 URL: https://doi.org/10.1109/apsipa.2017.8282121 Collector: openalex OA: false 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 NVIDIA DIGITS DevBox and Titan X GPU).