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Investigation of Unsafe Construction Site Conditions Using Deep Learning Algorithms Using Unmanned Aerial Vehicles.

Sourav Kumar¹, Mukilan Poyyamozhi¹, Balasubramanian Murugesan¹

¹Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India.

Sensors (Basel, Switzerland)

|October 26, 2024

Summary

This summary is machine-generated.

This study uses Unmanned Aerial Vehicles (UAVs) with Faster R-CNN to monitor construction workers

Keywords:

Unmanned Aerial Vehicle automatic detection image recognition object detection tensor flow unsafe site conditions

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Area of Science:

Construction Safety
Robotics
Computer Vision

Background:

Unmanned Aerial Vehicles (UAVs) are increasingly adopted in construction for safety and monitoring.
Falls from height and falling objects are major risks in construction.
Ensuring Personal Protective Equipment (PPE) compliance, like helmets, is crucial for worker safety.

Purpose of the Study:

To leverage UAV technology for enhanced labor safety in construction.
To develop a system for real-time monitoring of construction workers' helmet usage.
To reduce risks of injuries and fatalities through automated safety inspections.

Main Methods:

Development of a UAV system integrated with the Faster R-CNN model and TensorFlow.
Real-time detection and identification of workers with and without helmets.
Implementation of an alert system for immediate feedback and intervention.

Main Results:

The UAV system achieved high precision (93.1%), recall, and processing speed (27 FPS).
Faster R-CNN demonstrated accurate detection of workers and helmet compliance across various site conditions.
Automated safety inspections reduced supervisor workload and improved monitoring efficiency.

Conclusions:

UAV technology offers a reliable and cost-effective solution for improving construction site safety.
The system enhances safety compliance and protects workers by ensuring PPE usage.
This approach significantly improves overall safety management quality in the construction industry.