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Flame Photometry: Overview01:02

Flame Photometry: Overview

573
Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
573
Flame Photometry: Lab01:16

Flame Photometry: Lab

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In a flame photometer, when a solution like potassium chloride is aspirated into the flame, the solvent evaporates, leaving behind dehydrated salt. This salt dissociates into free gaseous atoms in their ground state. Some of these atoms absorb energy from the flame, leading to their excitation. The excited atoms return to the ground state, emitting photons at characteristic wavelengths. Because only electronic transitions are involved, the resulting emission lines are very narrow. The intensity...
244
Gas Chromatography: Types of Detectors-II01:19

Gas Chromatography: Types of Detectors-II

368
In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...
368
Atomic Fluorescence Spectroscopy01:29

Atomic Fluorescence Spectroscopy

291
Atomic fluorescence spectroscopy (AFS) is an analytical technique that involves the electronic transitions of atoms in a flame, furnace, or plasma being excited by electromagnetic (EM) radiation. When these atoms absorb energy, they become excited and subsequently release energy as they return to their original state. This emitted light, or "fluorescence," is observed at a right angle to the incident beam. Both absorption and emission processes transpire at distinct wavelengths, which...
291
Atomic Emission Spectroscopy: Interference01:30

Atomic Emission Spectroscopy: Interference

183
In atomic emission spectroscopy (AES), high-temperature atomizers excite a broad range of elements and molecules that generate complex emissions from sources such as oxides, hydroxides, and flame combustion products in the flame or plasma. Several strategies can be employed to minimize spectral interferences caused by overlapping emission lines or bands. These include increasing instrument resolution, choosing alternative emission lines, optimally placing the detector in low-background regions,...
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Updated: Jun 28, 2025

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A Real-Time Detection Algorithm of Flame Target Image.

Jing Zhao1

  • 1School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing 210094, China.

Computational Intelligence and Neuroscience
|April 18, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces YOLO+, a novel algorithm for real-time flame detection, significantly improving accuracy and speed for small objects in supply chains. The enhanced method achieves 99.5% accuracy with a low omission rate.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Industrial Safety

Background:

  • Accurate and rapid flame detection, particularly for small objects within supply chains, presents a persistent challenge in research.
  • Existing algorithms often struggle with the speed and precision required for real-time applications.

Purpose of the Study:

  • To develop a new real-time target detection algorithm specifically for enhanced flame recognition of small objects.
  • To improve the accuracy and efficiency of flame detection systems in supply chain environments.

Main Methods:

  • Implemented multi-scale feature fusion to strengthen feature extraction for small object recognition.
  • Integrated K-means clustering into the prior bounding box to enhance detection accuracy.
  • Utilized specific flame characteristics within the YOLO+ algorithm to minimize false positives and improve detection efficacy.

Main Results:

  • The YOLO+ algorithm achieved a high accuracy rate of 99.5%.
  • Demonstrated a low omission rate of 1.3%.
  • Reached a detection speed of 72 frames per second.

Conclusions:

  • The proposed YOLO+ algorithm offers superior performance compared to traditional YOLO series algorithms for flame detection tasks.
  • The algorithm's speed and accuracy make it highly suitable for real-time flame detection in supply chain applications.
  • The study highlights the effectiveness of multi-scale fusion and K-means clustering in improving small object detection.