Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

197
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
197
Design Example: Measuring Distance Between Two Points with Obstructions01:10

Design Example: Measuring Distance Between Two Points with Obstructions

239
When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
239

You might also read

Related Articles

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

Sort by
Same author

Machine-Learning-Enabled Rapid Evolution of Photoenzymes for the Asymmetric Synthesis of gem-Difluorophosphonates.

Angewandte Chemie (International ed. in English)·2026
Same author

Severe hypokalemia induced by ceftazidime-avibactam: Case report.

Medicine·2026
Same author

The Subepithelial Bandlike Distribution Pattern of the CD4 Biomarker May Determine Oral Lichen Planus in the Absence of Typical Microscopic Features.

International journal of molecular sciences·2026
Same author

Transposon-based genome editing of industrial microorganisms: advances, challenges, and prospects.

Synthetic and systems biotechnology·2026
Same author

Structure-guided surface engineering to improve the catalytic activity of ethylene-forming enzyme.

Engineering microbiology·2026
Same author

Deep knowledge-driven multi-modal fusion for diagnosis and prognosis of SI-ARDS.

Communications medicine·2026

Related Experiment Video

Updated: Nov 25, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.3K

Implementation of a Lightweight Semantic Segmentation Algorithm in Road Obstacle Detection.

Bushi Liu1, Yongbo Lv1, Yang Gu1

  • 1School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.

Sensors (Basel, Switzerland)
|December 16, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces SP-ICNet, a lightweight deep learning model for real-time semantic segmentation. It enhances obstacle detection and road recognition for autonomous driving systems, improving safety and efficiency.

Keywords:
deep learningloss functionroad obstacle detectionsemantic segmentationspatial information network

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

817
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.2K

Related Experiment Videos

Last Updated: Nov 25, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.3K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

817
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.2K

Area of Science:

  • Computer Vision
  • Deep Learning
  • Autonomous Driving Systems

Background:

  • Semantic segmentation is crucial for autonomous driving, enabling collision avoidance by identifying obstacles.
  • Current semantic segmentation methods face challenges like complex network depth, large datasets, and real-time processing demands.

Purpose of the Study:

  • To develop an improved, lightweight, real-time semantic segmentation network for autonomous driving applications.
  • To enhance the accuracy and efficiency of obstacle detection and drivable area recognition.

Main Methods:

  • An efficient Image Cascading Network (ICNet) architecture was adapted and improved.
  • Multi-scale branches and cascaded feature fusion were employed for rich feature extraction.
  • A spatial information network was integrated to improve prior knowledge of spatial location and edge details.
  • An external loss function was appended during training to boost the learning process.

Main Results:

  • The proposed SP-ICNet model demonstrated substantial performance on the Cityscapes dataset.
  • The network achieved real-time performance while enhancing the accuracy of road obstacle detection.
  • Experimental comparisons confirmed superior performance compared to existing popular semantic segmentation networks.

Conclusions:

  • The lightweight SP-ICNet effectively addresses challenges in real-time semantic segmentation for autonomous driving.
  • The model provides accurate obstacle detection and road segmentation, crucial for safe autonomous navigation.
  • SP-ICNet offers a promising solution for enhancing the perception capabilities of autonomous vehicles.