See clearer at night: towards robust nighttime semantic segmentation through day-night image conversion

@inproceedings{Sun2019SeeCA,
  title={See clearer at night: towards robust nighttime semantic segmentation through day-night image conversion},
  author={Lei Sun and Kaiwei Wang and Kailun Yang and Kaite Xiang},
  booktitle={Security + Defence},
  year={2019}
}
  • Lei Sun, Kaiwei Wang, +1 author Kaite Xiang
  • Published in Security + Defence 2019
  • Computer Science, Engineering
  • In recent years, intelligent driving navigation and security monitoring have made considerable progress with the help of deep Convolutional Neural Networks (CNNs). As one of the state-of-the-art perception approaches, semantic segmentation unifies distinct detection tasks widely desired by both autonomous driving and security monitoring. Currently, semantic segmentation shows remarkable efficiency and reliability in standard scenarios such as daytime scenes with favorable illumination… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 48 REFERENCES

    BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling

    VIEW 8 EXCERPTS
    HIGHLY INFLUENTIAL

    A Cross-Season Correspondence Dataset for Robust Semantic Segmentation

    A multimodal vision sensor for autonomous driving

    Depth-Attentional Features for Single-Image Rain Removal

    VIEW 1 EXCERPT

    Don’t Worry About the Weather: Unsupervised Condition-Dependent Domain Adaptation

    VIEW 1 EXCERPT