Summary of Certified Ml Object Detection For Surveillance Missions, by Mohammed Belcaid (c-s Group) et al.
Certified ML Object Detection for Surveillance Missions
by Mohammed Belcaid, Eric Bonnafous, Louis Crison, Christophe Faure, Eric Jenn, Claire Pagetti
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 This paper presents a development process for a drone detection system that incorporates machine learning-based object detection. The goal is to achieve acceptable performance levels and provide sufficient evidence to meet the recommendations outlined in the forthcoming ED 324/ARP 6983 standard, thereby establishing confidence in the dependability of the designed system. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us build a better drone detection system using special computer vision techniques. It’s like having a superpower to detect drones accurately and quickly. The team worked hard to make sure their system meets important standards so we can trust it to work well. |
Keywords
» Artificial intelligence » Machine learning » Object detection
