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Related Concept Videos

Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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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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Related Experiment Video

Updated: Feb 18, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor.

Toan Minh Hoang1, Na Rae Baek2, Se Woon Cho3

  • 1Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea. hoangminhtoan@dongguk.edu.

Sensors (Basel, Switzerland)
|November 17, 2017
PubMed
Summary

This study introduces a novel method for robust road lane detection in autonomous vehicles, effectively addressing challenges posed by shadows and poor illumination. The approach enhances safety systems by improving lane recognition accuracy under adverse conditions.

Keywords:
fuzzy systemline segment detectorroad lane detectionshadows

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Autonomous vehicles rely heavily on accurate road lane detection for safe operation.
  • Environmental factors like shadows, occlusion, and poor lighting degrade lane marking visibility, posing a significant challenge.
  • Existing lane detection methods struggle with variable illumination conditions.

Purpose of the Study:

  • To develop an improved road lane detection method resilient to illumination variations, especially severe shadows.
  • To enhance the reliability of lane departure warning systems and autonomous driving functionalities.
  • To provide a robust solution for visible light camera-based lane detection.

Main Methods:

  • A novel approach combining fuzzy systems and line segment detector algorithms was proposed.
  • The method specifically targets overcoming illumination challenges, including harsh shadows.
  • Utilized visible light camera sensor data for lane detection.

Main Results:

  • The proposed method demonstrated superior performance in detecting road lanes compared to conventional techniques.
  • Experiments were conducted using three diverse open datasets: Caltech, Santiago Lanes Dataset (SLD), and Road Marking Dataset.
  • Significant improvements in lane detection accuracy were observed under challenging lighting conditions.

Conclusions:

  • The developed fuzzy system and line segment detector algorithm effectively enhances road lane detection accuracy.
  • This method offers a reliable solution for autonomous vehicle safety systems operating in diverse and challenging environments.
  • The findings suggest a promising advancement in computer vision for autonomous driving applications.