Summary of Allweathernet:unified Image Enhancement For Autonomous Driving Under Adverse Weather and Lowlight-conditions, by Chenghao Qian et al.
AllWeatherNet:Unified Image Enhancement for Autonomous Driving under Adverse Weather and Lowlight-conditions
by Chenghao Qian, Mahdi Rezaei, Saeed Anwar, Wenjing Li, Tanveer Hussain, Mohsen Azarmi, Wei Wang
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel computer vision method, AllWeather-Net, to improve the visual quality and clarity of images degraded by adverse conditions like snow, rain, nighttime, and fog. The hierarchical architecture incorporates information at three semantic levels: scene, object, and texture. A Scaled Illumination-aware Attention Mechanism (SIAM) guides the learning towards road elements critical for autonomous driving perception. AllWeather-Net demonstrates superior image enhancement results, improving semantic segmentation performance by up to 5.3% in the trained domain. The model also generalizes well to unseen domains without re-training, achieving up to 3.9% mIoU improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem for self-driving cars: how to make images taken in bad weather (like snow or rain) look normal again. The authors created a new computer program called AllWeather-Net that can do this. It looks at three different levels: what’s happening in the scene, what objects are there, and what textures they have. This helps it focus on the important parts of the image, like roads and signs. The program is really good at making images look normal again, which makes it better for self-driving cars to use. |
Keywords
» Artificial intelligence » Attention » Semantic segmentation
