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Real-time traffic sign detection based on YOLOv2
Author(s): Huan Zhu; Chongyang Zhang
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Paper Abstract

Traffic sign detection is an important part of driverless vehicle. high accuracy detection algorithms are difficult to run in real-time. In this paper, we propose a detection model to ease the problem effectively. our model combines three key insights with YOLOv2 to improve the mean average precision(mAP): (1) Focal Loss is used to let our model focus on a sparse set of indistinguishable samples, (2) Inception is used to increase the depth and nonlinearity of network and (3) ResNet is used to ease the difficult in training deep convolutional neural network by adding cross-layer connections. On the German Traffic Sign Detection Benchmark (GTSDB), our model can achieve high accuracy and real-time performance of traffic sign detection at the same time. The recall is 94.46%, the precision is 96.60%, the AUC is 99.75%, the mAP is 88.23% and the average time for processing an image is 0.017s. Results indicate that the modified detection model is competitive compared to others.

Paper Details

Date Published: 29 October 2018
PDF: 9 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108361B (29 October 2018); doi: 10.1117/12.2513869
Show Author Affiliations
Huan Zhu, Nanjing Univ. of Science and Technology (China)
Chongyang Zhang, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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