Summary of Adver-city: Open-source Multi-modal Dataset For Collaborative Perception Under Adverse Weather Conditions, by Mateus Karvat et al.
Adver-City: Open-Source Multi-Modal Dataset for Collaborative Perception Under Adverse Weather Conditions
by Mateus Karvat, Sidney Givigi
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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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 introduces Adver-City, an open-source synthetic dataset for Collaborative Perception (CP) in adverse weather conditions. The dataset is designed to challenge CP models and improve their performance in real-world scenarios. It contains over 24 thousand frames, with annotations of 110 unique scenarios across six different weather conditions: clear weather, soft rain, heavy rain, fog, foggy heavy rain, and glare. The scenarios are based on real crash reports and feature varying object densities, allowing for novel testing conditions. Benchmarks run on the dataset show that CP models performed poorly in adverse weather conditions, with a reduction in multi-modal object detection performance by up to 19%. Object density also affected LiDAR-based detection, reducing it by up to 29%. The dataset, code, and documentation are available at https://labs.cs.queensu.ca/quarrg/datasets/adver-city/. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a special kind of computer data called Adver-City. This data is used for training machines that can see in bad weather like rain or fog. It’s the first time this type of data has been made, and it includes lots of different scenarios where objects are either close together or far apart. The people who created this data wanted to make sure it was realistic, so they based it on real accidents that happened on the road. They found that machines had trouble seeing things in bad weather, with some mistakes being as high as 19%. This data is important because it can help us create better machines that can see and drive safely in all kinds of conditions. |
Keywords
» Artificial intelligence » Multi modal » Object detection
