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oapen-20.500.12657-890652024-04-03T02:23:05Z Chapter Computer Vision-Based Monitoring Framework for Forklift Safety at Construction Site Abbas, Muhammad Sibtain Khan, Nasrullah Sabir, Aqsa Zaidi, Syed Farhan Alam Hussain, Rahat Yang, Jaehun Park, Chansik 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 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 2024-04-02T15:45:25Z 2024-04-02T15:45:25Z 2023 chapter ONIX_20240402_9791221502893_34 2704-5846 9791221502893 https://library.oapen.org/handle/20.500.12657/89065 eng Proceedings e report application/pdf n/a 9791221502893_67.pdf https://books.fupress.com/doi/capitoli/979-12-215-0289-3_67 Firenze University Press 10.36253/979-12-215-0289-3.67 10.36253/979-12-215-0289-3.67 bf65d21a-78e5-4ba2-983a-dbfa90962870 9791221502893 137 7 Florence open access
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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
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