Journal
NEURAL COMPUTING & APPLICATIONS
Volume 32, Issue 11, Pages 6533-6543Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-019-04086-z
Keywords
Traffic sign; Object detection; Feature pyramid
Categories
Funding
- National Natural Science Foundation of China [61672133, 61832001, 61632007]
Ask authors/readers for more resources
With the development of driverless technology, we are in dire need of a method to understand traffic scenes. However, it is still a difficult task to detect traffic signs because of the tiny scale of signs in real-world images. In complex scenarios, some traffic signs could be very elusive due to the awful weather and lighting conditions. To implement a more comprehensive detection and recognition system, we develop a two-stage network. At the region proposal stage, we adopt a deep feature pyramid architecture with lateral connections, which makes the semantic feature of small object more sensitive. At the classification stage, densely connected convolutional network is used to strengthen the feature transmission and multiplexed, which leads to more accurate classification with less number of parameters. We test on GTSDB detection benchmark, as well as the challenging Tsinghua-Tencent 100K benchmark which is pretty difficult for most traditional networks. Experiments show that our proposed method achieves a very great performance and surpasses the other state-of-the-art methods. Implementation source code is available at https://github.com/derderking/Traffic-Sign.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available