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Title: A highly efficient vehicle taillight detection approach based on deep learning
Authors: Li, Qiaohong
Garg, Sahil
Nie, Jiangtian
Li, Xiang
Liu, Ryan Wen
Cao, Zhiguang
Hossain, M. Shamim
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Li, Q., Garg, S., Nie, J., Li, X., Liu, R. W., Cao, Z. & Hossain, M. S. (2020). A highly efficient vehicle taillight detection approach based on deep learning. IEEE Transactions On Intelligent Transportation Systems, 22(7), 4716-4726.
Project: R266000096133
Journal: IEEE Transactions on Intelligent Transportation Systems
Abstract: Vehicle taillight detection is essential to analyze and predict driver intention in collision avoidance systems. In this article, we propose an end-to-end framework that locates the rear brake and turn signals from video stream in real-time. The system adopts the fast YOLOv3-tiny as the backbone model and three improvements have been made to increase the detection accuracy on taillight semantics, i.e., additional output layer for multi-scale detection, spatial pyramid pooling (SPP) module for richer deep features, and focal loss for alleviation of class imbalance and hard sample classification. Experimental results demonstrate that the integration of multi-scale features as well as hard examples mining greatly contributes to the turn light detection. The detection accuracy is significantly increased by 7.36%, 32.04% and 21.65% (absolute gain) for brake, left-turn and right-turn signals, respectively. In addition, we construct the taillight detection dataset, with brake and turn signals are specified with bounding boxes, which may help nourishing the development of this realm.
ISSN: 1524-9050
DOI: 10.1109/TITS.2020.3027421
Schools: Interdisciplinary Graduate School (IGS) 
School of Computer Science and Engineering 
Research Centres: Energy Research Institute @ NTU (ERI@N) 
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:ERI@N Journal Articles
IGS Journal Articles
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