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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

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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.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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Lossy Lines and Overvoltages01:22

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Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
Attenuation
When constant series resistance and shunt conductance are present, voltage and current equations are modified. The propagation constant indicates that voltage and current waves consist of both forward and backward traveling components. These waves attenuate as they propagate, with the attenuation factor related to the resistance and conductance. In a...
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Difference from Background: Limit of Detection01:05

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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.
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Line Loss01:10

Line Loss

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The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
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Lumber Defects01:23

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Lumber defects, which can affect both the appearance and structural integrity of wood, include a variety of growth and manufacturing flaws. Growth defects such as knots and knotholes occur where branches were once attached to the tree trunk, with knotholes forming when these knots fall out. Other natural defects include decay and insect damage, which compromise the wood's strength and durability.
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Related Experiment Video

Updated: Apr 15, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
11:34

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

11.6K

Lightweight Power Line Defect Detection Based on Improved YOLOv8n.

Yuhan Yin1, Xiaoyi Liu2, Kunxiao Wu1

  • 1School of Electrical Engineering, Southeast University, Nanjing 210096, China.

Sensors (Basel, Switzerland)
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a lightweight YOLOv8n model for efficient UAV-based power-line defect detection, achieving high accuracy with fewer parameters and computations. The improved model enhances detection performance in complex environments.

Keywords:
attention mechanismimproved YOLOv8nlightweight modelloss functionpower line defect detection

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11.6K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Electrical Engineering

Background:

  • UAV-based power-line defect detection faces challenges including small targets, background clutter, and high deployment costs.
  • Existing methods often struggle to balance detection accuracy with computational efficiency for real-world deployment.
  • Need for lightweight, accurate models for automated inspection of critical infrastructure.

Purpose of the Study:

  • To propose a lightweight defect detection model based on an improved YOLOv8n for UAV-based power-line inspection.
  • To enhance detection accuracy and efficiency, addressing challenges of small targets and complex backgrounds.
  • To achieve a superior balance between model performance and computational resources.

Main Methods:

  • Introduced an improved lightweight adaptive downsampling module (ADownPro) using dual-branch parallel structure and depthwise separable convolutions (DSConv).
  • Developed a cross-stage partial connections and partial convolution (CSPPC) module for efficient multi-scale feature fusion.
  • Integrated mixed local channel attention (MLCA) and a scale-annealed mixed-quality EIoU loss (SAMQ-EIoU) for improved localization and feature representation.

Main Results:

  • The improved YOLOv8n model achieved 91.4% mAP@0.50 and 64.5% mAP@0.50:0.95 on a constructed dataset.
  • The model boasts only 1.59 M parameters and 4.9 GFLOPs, demonstrating a significant lightweight design.
  • Outperformed mainstream detectors and recent models like YOLOv8n-DSN and IDD-YOLO in accuracy and efficiency, with improved robustness and generalization shown on public datasets.

Conclusions:

  • The proposed lightweight YOLOv8n model effectively addresses the challenges in UAV-based power-line defect detection.
  • The novel modules (ADownPro, CSPPC, MLCA, SAMQ-EIoU) contribute to enhanced accuracy and efficiency.
  • The model offers a promising solution for practical, cost-effective automated power-line inspection.