adjacent infrared multitarget detection using robust background estimation
Clicks: 173
ID: 130367
2016
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
30.0
/100
173 views
43 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #94 of 205 articles by views in BMC infectious diseases
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 205 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Small target detection is very important for infrared search and track (IRST) problems. Grouped targets are difficult to detect using the conventional constant false alarm rate (CFAR) detection method. In this study, a novel multitarget detection method was developed to identify adjacent or closely spaced small infrared targets. The neighboring targets decrease the signal-to-clutter ratio in hysteresis threshold-based constant false alarm rate (H-CFAR) detection, which leads to poor detection performance in cluttered environments. The proposed adjacent target rejection-based robust background estimation can reduce the effects of the neighboring targets and enhance the small multitarget detection performance in infrared images by increasing the signal-to-clutter ratio. The experimental results of the synthetic and real adjacent target sequences showed that the proposed method produces an upgraded detection rate with the same false alarm rate compared to the recent target detection methods (H-CFAR, Top-hat, and TDLMS).
| Reference Key |
kim2016journaladjacent
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Sungho Kim;Kyung-Tae Kim |
| Journal | BMC infectious diseases |
| Year | 2016 |
| DOI |
10.1155/2016/7279081
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.