Browsing by Author "Lin, Che-Tsung"

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One Stage Detection Network with an Auxiliary Classifier for Real-Time Road Marks Detection
(2018-01-01)
Lin, Guan-Ting
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Santoso, Patrisia Sherryl
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Lin, Che-Tsung
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Tsai, Chia-Chi
;
Guo, Jiun-In
We construct a robust road mark detector that achieves high accuracy with real-time processing performance ( 32 fps) under nVidia Titan-X GPU. We combine one stage deep learning detector with auxiliary CNN classifiers as a robust road marks detector. We found out that one stage detector not only detects multiple objects via single inference efficiently, but also remains a good accuracy in performance perspective. However, to make it better, we add an extra CNN classifier as the back part of the proposed architecture to reduce false positive and get better accuracy. The proposed detector can achieve 86.8% mAP in our in-house six-class road mark database.
RECOGNIZING CHINESE TEXTS WITH 3D CONVOLUTIONAL NEURAL NETWORK
(2019-01-01)
Chen, Kuan-Chou
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Lin, Guan-Ting
;
Lin, Che-Tsung
;
Guo, Jiun-In
;
交大名義發表
In this paper, we propose a deep learning system to localize and recognize Chinese texts in scenes with signage and road marks through 3D convolutional neural network. The proposed system adopts YOLO for detecting target location and exploits 3D convolutional neural network for recognizing the contents. The proposed design outperforms the existing designs based on LSTM and achieves real-time processing performance, which is feasible to be implemented on embedded platforms. The proposed system reaches over 90% accuracy in recognizing Chinese texts on bird's-eye viewing road marks in a self-driving vehicle equipped with a fisheye camera. In addition, this system can achieve 20 fps execution speed with NVIDIA DIGITS DevBox with 1080Ti GPU, which is fast enough for autonomous driving applications.
Stop Line Detection and Distance Measurement for Road Intersection based on. Deep Learning Neural Network
(2017-01-01)
Lin, Guan-Ting
;
Santoso, Patrisia Sherryl
;
Lin, Che-Tsung
;
Tsai, Chia-Chi
;
Guo, Jiun-In
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).