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Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Flame Photometry: Overview01:02

Flame Photometry: Overview

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...
Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Flame Photometry: Lab01:16

Flame Photometry: Lab

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...
Detection of Black Holes01:10

Detection of Black Holes

Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
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Not until the 1960s, when the first neutron...
Reducing Line Loss01:18

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Related Experiment Videos

Forest Fire Detection Based on Improved YOLO11.

Jialong Gao1, Yanqiao Zhao1, Bowen Chen1

  • 1School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an improved YOLOv11 model for efficient forest fire detection. The enhanced model achieves faster speeds and better accuracy, making forest fire detection systems more lightweight and effective.

Keywords:
SPD-ConvShuffleNetV1YOLO11forest fire detectionlightweight network

Related Experiment Videos

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Environmental Monitoring

Background:

  • Existing forest fire detection systems face challenges with model size and accuracy.
  • Lightweight and accurate detection models are crucial for real-time forest fire monitoring.

Purpose of the Study:

  • To develop a refined forest fire detection approach using an improved YOLOv11 architecture.
  • To enhance model lightweighting and improve detection accuracy for forest fire targets.

Main Methods:

  • Modified the YOLOv11 backbone network by integrating the ShuffleNetV1 module for efficient deployment.
  • Incorporated the SPD-Conv convolutional module to improve feature aggregation for large flames and preserve details for small smoke targets.

Main Results:

  • Achieved a real-time inference speed of 148.3 FPS.
  • Reduced parameter count by 22.5% and GFLOPs by 15.0%.
  • Improved mean Average Precision (mAP) by 0.3%.

Conclusions:

  • The improved YOLOv11 model successfully achieves lightweight design and enhanced detection accuracy for forest fires.
  • This approach offers a more efficient and effective solution for forest fire detection systems.