Summary of New Foggy Object Detecting Model, by Rahul Banavathu et al.
New Foggy Object Detecting Model
by Rahul Banavathu, Modem Veda Sree, Bollina Kavya Sri, Suddhasil De
First submitted to arxiv on: 27 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed paper introduces a novel approach to object detection in reduced visibility scenarios, which is crucial for various applications. A two-staged architecture is designed, comprising region identification from input images and subsequent object detection within these regions. The methodology demonstrates significant improvements in accuracy and detection time compared to existing techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Object detection gets tricky when it’s hard to see! Scientists have been working on ways to make computers better at finding things even when it’s foggy or dark. This new method uses a special two-step approach that first identifies regions where objects might be, and then looks for those objects within those areas. It seems to work really well and could help with all sorts of tasks like self-driving cars and surveillance systems. |
Keywords
* Artificial intelligence * Object detection
