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            Chapter Computer Vision-Based Monitoring Framework for Forklift Safety at Construction Site

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            Author(s)
            Abbas, Muhammad Sibtain
            Khan, Nasrullah
            Sabir, Aqsa
            Zaidi, Syed Farhan Alam
            Hussain, Rahat
            Yang, Jaehun
            Park, Chansik
            Language
            English
            Show full item record
            Abstract
            Efficient forklift operation is critical for construction site safety and project progress; yet, the construction industry deals with recurrent issues, including unauthorized forklift operation, operator drowsiness, visibility challenges, blind spots, and load placement errors. This paper introduces the "iSafe ForkLift," a comprehensive safety framework powered by computer vision, specifically designed to tackle these multifaceted safety challenges associated with forklift operations. The framework provides an array of integrated solutions, encompassing facial recognition for authorization, anomaly detection for behavior monitoring, stereo cameras for improved visibility, blind spot solutions, and load placement monitoring. Aligned with OSHA safety standards, it offers opportunities for enhanced forklift safety by addressing a broad spectrum of potential risks within a single, efficient framework. Systematically addressing multiple safety risks within this unified framework significantly elevates overall safety. Future studies should prioritize enhancing technology by merging computer vision with IoT to boost precision and safety, especially on challenging terrains, thereby elevating construction industry standards' reliability
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/175715
            Keywords
            Forklift operations; Computer vision; Safety framework; Operator drowsiness; Visibility challenges; OSHA standards; Regulatory compliance; thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization
            DOI
            10.36253/979-12-215-0289-3.67
            ISBN
            9791221502893
            Publisher
            Firenze University Press
            Publisher website
            www.fupress.com/
            Publication date and place
            Florence, 2023
            Series
            Proceedings e report,
            Pages
            7
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              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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