Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Light Acquisition

Light Acquisition

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.

Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

Photoreceptors and Visual Pathways

Photoreceptors and Visual Pathways

At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Adversarial CAM Guidance for Chest X-Ray Classification: Reducing Framing Sensitivity with Mask Supervision.

Biomimetics (Basel, Switzerland)·2026

Same author

KDOSS-net: Knowledge distillation-based outpainting and semantic segmentation network for crop and weed images.

Plant phenomics (Washington, D.C.)·2025

Same author

WRA-Net: Wide Receptive Field Attention Network for Motion Deblurring in Crop and Weed Image.

Plant phenomics (Washington, D.C.)·2023

Same author

Human Blastocyst Components Detection Using Multiscale Aggregation Semantic Segmentation Network for Embryonic Analysis.

Biomedicines·2022

Same author

Deep Features Aggregation-Based Joint Segmentation of Cytoplasm and Nuclei in White Blood Cells.

IEEE journal of biomedical and health informatics·2022

Same author

DMDF-Net: Dual multiscale dilated fusion network for accurate segmentation of lesions related to COVID-19 in lung radiographic scans.

Expert systems with applications·2022

Search research articles

Related Experiment Video

Updated: Mar 16, 2026

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

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

Published on: January 20, 2023

Road Lane Detection by Discriminating Dashed and Solid Road Lanes Using a Visible Light Camera Sensor.

Toan Minh Hoang¹, Hyung Gil Hong², Husan Vokhidov³

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)

|August 23, 2016

Summary

This study introduces a novel road lane detection method that accurately identifies lane boundaries and differentiates between dashed and solid lines, enhancing driving safety for both autonomous and human-driven vehicles.

Keywords:

autonomous vehicles dashed and solid road lanes left and right boundaries of road lane road lane detection

More Related Videos

Light Spot-Based Assay for Analysis of Drosophila Larval Phototaxis

Light Spot-Based Assay for Analysis of Drosophila Larval Phototaxis

Published on: September 27, 2019

Visually Mediated Odor Tracking During Flight in Drosophila

Visually Mediated Odor Tracking During Flight in Drosophila

Published on: January 26, 2009

Related Experiment Videos

Last Updated: Mar 16, 2026

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

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

Published on: January 20, 2023

Light Spot-Based Assay for Analysis of Drosophila Larval Phototaxis

Light Spot-Based Assay for Analysis of Drosophila Larval Phototaxis

Published on: September 27, 2019

Visually Mediated Odor Tracking During Flight in Drosophila

Visually Mediated Odor Tracking During Flight in Drosophila

Published on: January 26, 2009

Area of Science:

Computer Vision
Robotics
Automotive Engineering

Background:

Road lane detection is critical for advanced driver-assistance systems (ADAS) and autonomous vehicles.
Existing methods often detect only the lane center, neglecting precise boundaries.
Current approaches fail to distinguish between dashed and solid lane markings, posing safety risks.

Purpose of the Study:

To develop an advanced road lane detection method.
To accurately identify both left and right lane boundaries.
To discriminate between dashed and solid lane markings for improved safety.

Main Methods:

A novel road lane detection algorithm was proposed.
The method focuses on precise boundary detection.
It incorporates logic to differentiate lane marking types (dashed vs. solid).

Main Results:

The proposed method accurately detects road lane boundaries.
It successfully distinguishes between dashed and solid lane markings.
Performance was validated using the Caltech open database.

Conclusions:

The new road lane detection method surpasses conventional approaches.
Accurate lane boundary and marking type detection significantly enhances driving safety.
This advancement is crucial for reliable autonomous vehicle operation.