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

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...

Field Application of Global Positioning System

Field Application of Global Positioning System

The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...

Local Attraction

Local Attraction

Local attraction refers to disturbances in compass readings caused by magnetic influences from nearby objects such as metal fences, buried pipes, vehicles, buildings, power lines, or natural iron ore deposits. Small items like wristwatches, steel tools, or belt buckles can also interfere with the compass by creating local magnetic fields that distort the Earth's natural magnetic field. These distortions lead to inaccurate readings, posing navigation and land surveying challenges.Local...

You might also read

Related Articles

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

Sort by

Same author

Improved Air Stability of Li Argyrodites Through PS<sub>4</sub> <sup>3-</sup> Rotation Suppression by Al and Se Co-Substitution for All-Solid-State Batteries.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025

Same author

Post-Treatment of Monolayer MoS<sub>2</sub> Field-Effect Transistors with H<sub>2</sub>O Vapor: Alleviation of Remote Channel Doping.

ACS applied materials & interfaces·2025

Same author

Effect of Starter Cultures on Quality of Fermented Sausages.

Food science of animal resources·2023

Same author

Damage-Detection Approach for Bridges with Multi-Vehicle Loads Using Convolutional Autoencoder.

Sensors (Basel, Switzerland)·2022

Same author

A Novelty Detection Approach for Tendons of Prestressed Concrete Bridges Based on a Convolutional Autoencoder and Acceleration Data.

Sensors (Basel, Switzerland)·2019

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 1, 2025

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

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

Published on: December 15, 2023

Improved Discriminative Object Localization Algorithm for Safety Management of Indoor Construction.

Jungeun Hwang¹, Kanghyeok Lee², May Mo Ei Zan¹

¹Department of Civil Engineering, Inha University, Incheon 22212, Republic of Korea.

Sensors (Basel, Switzerland)

|April 28, 2023

Summary

This summary is machine-generated.

An improved discriminative object localization (IDOL) algorithm enhances safety management by accurately visualizing indoor construction sites. This computer vision approach offers better object localization than existing methods, reducing accidents.

Keywords:

indoor construction site object localization safety management small construction tool visual explanation

More Related Videos

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

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

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Related Experiment Videos

Last Updated: Aug 1, 2025

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

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

Published on: December 15, 2023

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

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

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Area of Science:

Computer Vision
Artificial Intelligence
Construction Safety

Background:

Occupational fatalities and accidents at indoor construction sites remain a significant concern.
Current safety management practices often rely on manual procedures, which can be inefficient and prone to errors.
Object localization technology is crucial for automated safety monitoring and risk assessment.

Purpose of the Study:

To introduce an improved discriminative object localization (IDOL) algorithm for enhanced indoor construction site safety management.
To provide safety managers with improved visualization tools for proactive risk identification.
To compare the performance of the IDOL algorithm against established object detection models.

Main Methods:

The IDOL algorithm utilizes Grad-CAM visualization images from the EfficientNet-B7 classification network.
It automatically identifies object characteristics without requiring additional annotations.
Performance was evaluated by comparing 2D localization accuracy and 3D localization error with the YOLOv5 model.

Main Results:

The IDOL algorithm demonstrated higher localization accuracy in 2D coordinates compared to YOLOv5.
It also exhibited a lower localization error in 3D coordinates than the YOLOv5 model.
IDOL provided more precise coordinate identification for objects in both 2D images and 3D point clouds.

Conclusions:

The IDOL algorithm offers superior object localization performance over the YOLOv5 model.
This enhanced capability can significantly aid in the visualization of indoor construction sites.
The IDOL algorithm has the potential to improve overall safety management and reduce occupational hazards.