Traffic Sign Classification Using Deep Inception Based Convolutional Networks

@article{Haloi2015TrafficSC,
  title={Traffic Sign Classification Using Deep Inception Based Convolutional Networks},
  author={Mrinal Haloi},
  journal={CoRR},
  year={2015},
  volume={abs/1511.02992}
}
In this work, we propose a novel deep network for traffic sign classification that achieves outstanding performance on GTSRB surpassing all previous methods. Our deep network consists of spatial transformer layers and a modified version of inception module specifically designed for capturing local and global features together. This features adoption allows our network to classify precisely intraclass samples even under deformations. Use of spatial transformer layer makes this network more… CONTINUE READING
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References

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Showing 1-10 of 15 references

Going deeper with convolutions

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) • 2015
View 7 Excerpts
Highly Influenced

A robust lane detection and departure warning system

2015 IEEE Intelligent Vehicles Symposium (IV) • 2015
View 3 Excerpts

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

2015 IEEE International Conference on Computer Vision (ICCV) • 2015
View 2 Excerpts

Driver-Activity Recognition in the Context of Conditionally Autonomous Driving

2015 IEEE 18th International Conference on Intelligent Transportation Systems • 2015
View 1 Excerpt

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