Extension usage examples:
Here's how our browser extension sees the article:
Video Flame and Smoke Based Fire Detection Algorithms: A Literature Review | SpringerLink
Source: link.springer.com
Article summary:
1. The International Association of Fire and Rescue Services (CTIF) reported 23,535 structure fire incidents in eighteen selected cities of the world in the year 2017.
2. Vision-based fire flame and smoke detection systems (VFSDS) utilize the detection of reflected and emitted light by an object to find its details.
3. Handcraft rules and classifiers based fire flame and smoke detection algorithms use features such as color, wavelet, texture, hog, motion direction, change in direction, speed, self-similarity property of smoke, etc.
Article analysis:
The article is overall reliable and trustworthy due to its comprehensive coverage of video flame and smoke based fire detection algorithms. The article provides a detailed overview of the current research work being done in this field as well as future directions for further development. It also provides a clear explanation of the different categories of vision-based smoke and flame detection systems such as patch level algorithms, blob level methods, pixel level methods, etc., along with their strengths and weaknesses.
However, there are some potential biases that should be noted when reading this article. For example, it does not provide any counterarguments or explore any unexplored perspectives on the topic which could lead to a one-sided reporting of the issue at hand. Additionally, some claims made in the article are not supported by evidence which could lead to confusion for readers who are unfamiliar with this topic. Furthermore, there is no mention of possible risks associated with these technologies which could be important for readers to consider before investing in them or using them for safety purposes. Lastly, while both sides are presented equally throughout most parts of the article there is a slight partiality towards certain technologies over others which could lead to bias when making decisions about which technology to use for fire detection purposes.