Summary of Surveying You Only Look Once (yolo) Multispectral Object Detection Advancements, Applications and Challenges, by James E. Gallagher et al.
Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications And Challenges
by James E. Gallagher, Edward J. Oughton
First submitted to arxiv on: 3 Sep 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 This paper provides a comprehensive review of multispectral imaging technologies and deep learning models for object detection, classification, and segmentation tasks in the non-visible light spectrum. The authors analyze 400 papers, focusing on 200 that employ You Only Look Once (YOLO) methods. Ground-based collection is the most prevalent approach, with 63% of reviewed papers utilizing this method. The study highlights the significance of YOLOv5 as the most used variant for adapting to multispectral applications, comprising 33% of modified YOLO models. China dominates multispectral-YOLO research, accounting for 58%, with similar research quality compared to other countries (mean journal impact factor: 4.45 vs. 4.36). The authors identify four key areas requiring future research: developing adaptive YOLO architectures, generating synthetic multispectral datasets, advancing transfer learning techniques, and innovating fusion research with other sensor types. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how we can use special cameras that capture images in different light spectrums to help machines like self-driving cars see better. The researchers reviewed hundreds of papers on this topic and found that a type of computer model called YOLO is really good at using these camera images to detect objects. They also discovered that most people working on this are doing it from China, but the quality of their work is similar to others around the world. The authors suggest that future research should focus on making these models more flexible, creating fake data sets for training, and improving how we combine information from different types of sensors. |
Keywords
» Artificial intelligence » Classification » Deep learning » Object detection » Transfer learning » Yolo
