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Traffic-light sign recognition using capsule network
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DOI：10.1007/s11042-020-10455-x.png)
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Automated driving gradually emerges as a real reality, but it still has to face various challenges, including sophisticated and volatile traffic conditions, human operating faults, etc. Amongst them, accurate understanding of traffic signs by using computer vision and deep learning methods has great significance for driving safety. In recent years, the advent of deep learning has made this issue much effectively. In this article, our major goal is to use deep learning models for conducting traffic-light sign recognition related to autonomous vehicles. As far as we know, this is the first time that the capsule neural network is employed as a method for scene understanding so as to effectively identify a class of traffic-light signs. Compared with the well-known convolutional neural networks, capsule networks diminish the demands for training datasets and tackle the spatial relationship much precisely.
Keyword:
Deep learning
Automatic driving
CapsNet
Traffic-light sign recognition
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3
论文数:
2.0W
被引数:
3.2W
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Optimized CapsNet for Traffic Jam Speed Prediction Using Mobile Sensor Data under Urban Swarming Transportation.png)
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