Work

Automated Object Detection and Tracking for Construction Site Safety

Year: 2024

Type: article

Abstract: The construction industry is increasingly adopting digital solutions to enhance safety, efficiency, and productivity. This project leverages the YOLOv8 object detection model and ByteTrack algorithm to track and count objects on construction sites. The system enables automated monitoring of personnel, machinery, and safety equipment through video analysis, addressing critical challenges like occlusions and dynamic object interactions. A custom dataset tailored for construction environments ensures high accuracy in detecting safety-critical items, such as personal protective equipment (PPE). This approach demonstrates significant potential for real-time safety assessments and operational optimization, serving as a technological leap in construction practices.

Source: International Journal for Research in Applied Science and Engineering Technology

DOI: 10.22214/ijraset.2024.65695

Author: Ms. Mamidi Sirisha

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: diamond

OpenAlex ID: W4404966976

FWCI: 0.144

Cites:

Cited by: 1

Related to: 10