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Surveying near highways, rough terrain, or power lines involves significant risks. Working along highways is particularly dangerous and requires the use of warning signs and flagmen. It is safest to avoid working directly on roads and use offsets whenever possible. When highway work is unavoidable, it must follow all safety guidelines. Surveyors should wear bright clothing, such as orange reflective vests, to ensure visibility to motorists, coworkers, and hunters. In construction zones, wearing...

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

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Construction Site Safety Management: A Computer Vision and Deep Learning Approach.

Jaekyu Lee¹, Sangyub Lee¹

¹Energy IT Convergence Research Center, Korea Electronics Technology Institute, Seongnam-si 13509, Republic of Korea.

Sensors (Basel, Switzerland)

|January 21, 2023

Summary

Image recognition technology enhances construction worker safety by monitoring presence, fall risks, and personal protective equipment compliance. This study introduces novel virtual environment verification methods for AI safety models.

Keywords:

image processing synthetic datasets transfer learning virtual datasets virtual validation environment worker safety management

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Design and Analysis for Fall Detection System Simplification

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Published on: April 6, 2020

Area of Science:

Computer Vision
Artificial Intelligence
Occupational Safety

Background:

Construction sites present significant safety risks, including falls and non-compliance with safety gear.
Traditional safety monitoring methods can be labor-intensive and may miss critical incidents.

Purpose of the Study:

To develop and validate image recognition models for improving construction worker safety.
To assess the feasibility of using virtual environments for training and verifying AI safety models.

Main Methods:

Developed three object recognition models: worker detection, fall risk assessment, and personal protective equipment (PPE) compliance.
Utilized transfer learning with real construction site imagery and synthetic data from virtual environments.
Employed virtual environments for efficient and safe simulation of accident scenarios for algorithm verification.

Main Results:

Successfully created AI models capable of identifying workers, assessing fall hazards, and verifying helmet and vest usage.
Demonstrated the effectiveness of virtual environments for generating training data and validating AI safety algorithms, particularly for rare events like falls.

Conclusions:

Image recognition technology offers a promising approach to proactively enhance construction site safety.
Virtual environment-based data generation and verification represent a significant advancement in developing robust AI safety solutions for hazardous industries.