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dc.contributor.authorAbbas, Muhammad Sibtain
dc.contributor.authorKhan, Nasrullah
dc.contributor.authorSabir, Aqsa
dc.contributor.authorZaidi, Syed Farhan Alam
dc.contributor.authorHussain, Rahat
dc.contributor.authorYang, Jaehun
dc.contributor.authorPark, Chansik
dc.date.accessioned2024-04-02T15:45:25Z
dc.date.available2024-04-02T15:45:25Z
dc.date.issued2023
dc.identifierONIX_20240402_9791221502893_34
dc.identifier.issn2704-5846
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/89065
dc.description.abstractEfficient 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
dc.languageEnglish
dc.relation.ispartofseriesProceedings e report
dc.subject.classificationthema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization
dc.subject.otherForklift operations
dc.subject.otherComputer vision
dc.subject.otherSafety framework
dc.subject.otherOperator drowsiness
dc.subject.otherVisibility challenges
dc.subject.otherOSHA standards
dc.subject.otherRegulatory compliance
dc.titleChapter Computer Vision-Based Monitoring Framework for Forklift Safety at Construction Site
dc.typechapter
oapen.identifier.doi10.36253/979-12-215-0289-3.67
oapen.relation.isPublishedBybf65d21a-78e5-4ba2-983a-dbfa90962870
oapen.relation.isbn9791221502893
oapen.series.number137
oapen.pages7
oapen.place.publicationFlorence


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