Unleashing Sustainable Efficiency: The Integration of Computer Vision into Industry 4.0

The Internet of Things, artificial intelligence, machine learning, and big data are just a few of the cutting-edge technologies that are being integrated into manufacturing processes as part of the "Industry 4.0" revolution. Computer vision is an essential component of Industry 4.0 regarding sustainability, developed as a disruptive technology that extracts and interprets visual information from digital photos or videos using image processing techniques and advanced models. In the context of Industry 4.0, this article offers an overview of computer vision, including its associated prospects, difficulties, and applications. A particular emphasis is placed on sustainability. It explores computer vision applications in robotics and automation, safety and security, process optimization, augmented reality, robotics and inspection, object identification and tracking, predictive maintenance, and quality control and inspection. The study also identifies the critical approaches used to overcome the difficulties in implementing computer vision solutions. Incorporating computer vision into Industry 4.0 holds promise for unleashing unprecedented levels of efficiency, novelty, and competitiveness in the industrial sector. The manufacturing and industrial sectors may use Industry 4.0's prospects and adopt sustainable practices by utilizing computer vision and overcoming its inherent limits. This will help to create an eco-conscious and efficient future.

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Publication Details

Journal
Engineering Management Journal
Published
2024-08-12
DOI
https://doi.org/10.1080/10429247.2024.2383518
Citations
4
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
1.40
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article

Unleashing Sustainable Efficiency: The Integration of Computer Vision into Industry 4.0

Susheela Hooda, Chaitanya Singla, Durgesh Srivastava, Rupali Gill et al.
4 citations
Engineering Management Journal
Digital Transformation in Industry
1.40
article

Unleashing Sustainable Efficiency: The Integration of Computer Vision into Industry 4.0

Susheela Hooda, Chaitanya Singla, Durgesh Srivastava, Rupali Gill, Rubina Chaudhary
article en
4 citations

Abstract

The Internet of Things, artificial intelligence, machine learning, and big data are just a few of the cutting-edge technologies that are being integrated into manufacturing processes as part of the "Industry 4.0" revolution. Computer vision is an essential component of Industry 4.0 regarding sustainability, developed as a disruptive technology that extracts and interprets visual information from digital photos or videos using image processing techniques and advanced models. In the context of Industry 4.0, this article offers an overview of computer vision, including its associated prospects, difficulties, and applications. A particular emphasis is placed on sustainability. It explores computer vision applications in robotics and automation, safety and security, process optimization, augmented reality, robotics and inspection, object identification and tracking, predictive maintenance, and quality control and inspection. The study also identifies the critical approaches used to overcome the difficulties in implementing computer vision solutions. Incorporating computer vision into Industry 4.0 holds promise for unleashing unprecedented levels of efficiency, novelty, and competitiveness in the industrial sector. The manufacturing and industrial sectors may use Industry 4.0's prospects and adopt sustainable practices by utilizing computer vision and overcoming its inherent limits. This will help to create an eco-conscious and efficient future.

Engineering Management JournalVol. 37(4)
Lovely Professional University (IN), Chitkara University (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 17%
Digital Transformation in Industry
1.40
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Unleashing Sustainable Efficiency: The Integration of Computer Vision into Industry 4.0 — Susheela Hooda, Chaitanya Singla, et al. · Engineering Management Journal (2024) | TGRS Research Map | TGRS