Affiliation:
1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
2. Key Laboratory of Big Data and Intelligent Robot, South China University of Technology, Ministry of Education, China
Abstract
Detecting vehicle at night is critical to both assistant driving systems and autonomous driving systems. In this paper, we propose a deep network scheme assisted by light information with good generalization to detect vehicle at night. Our approach is divided into two branches, the object stream and the pixel stream. The object stream generates a batch of bounding boxes, and the pixel stream utilizes the vehicle light information to calibrate the bounding boxes of the object stream. In the object stream, we propose a new structure, Direction Attention Pooling (DAP), to improve the accuracy of the prior boxes. DAP leads into attention mechanism. The feature maps obtained from backbone network is divided into two branches. One branch obtains direction perception information through IRNN layer, and the other branch learns attention weights. The weights are multiplied with the direction perception features in an element-wise manner. In the pixel stream, we propose a corner localization algorithm based on Bayes to get more accurate corners with the vehicle light pixels. The locations of the corners are considered as a discrete random variable. When the mask of the object is known, solving the probability distribution of the corner of the object is the next step. The corners with the highest probability is the correct corner. On the nighttime vehicle detection datasets CHUK and SYSU, our method achieves the accuracy of 97.2% and 96.86%, which outperforms other state-of-the-art methods by at least 0.31% and 0.34%.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Reference20 articles.
1. National Highway Traffic Safety Administration. National highway traffic safety administration’s traffic safety facts annual report. Technical report, 2018.
2. Fatal accidents in nighttime vs. daytime highway construction work zones;Arditi;Journal of Safety Research,2007
3. Injury severity analysis of accidents involving young male drivers in great britain;Gray;J Safety Res,2008
4. Face description with local binary patterns: Application to face recognition;Ahonen;IEEE Transactions on Pattern Analysis & Machine Intelligence,2006
5. Combining region-of-interest extraction and image enhancement for nighttime vehicle detection;Kuang;IEEE Intelligent Systems,2016
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