Summary of Fogguard: Guarding Yolo Against Fog Using Perceptual Loss, by Soheil Gharatappeh et al.
FogGuard: guarding YOLO against fog using perceptual loss
by Soheil Gharatappeh, Sepideh Neshatfar, Salimeh Yasaei Sekeh, Vikas Dhiman
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 FogGuard is a new type of object detection network specifically designed for use in foggy environments. The problem it tackles is that existing deep learning-based object detection models can struggle with accuracy when operating in foggy conditions, which is a critical issue for autonomous driving applications. FogGuard employs novel techniques to adapt to the reduced visibility caused by fog and improve object detection performance under these conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FogGuard is a special kind of computer program that helps cars see better on foggy days. Right now, many self-driving car systems rely on really smart computers that can recognize objects, but when it’s foggy outside, those computers get confused and have trouble doing their job. FogGuard is a new way to make those computers work better in the fog. |
Keywords
* Artificial intelligence * Deep learning * Object detection
