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@article{203162,
        author = {Shafaque Aziz and Intesar Fatima and Pawan Kumar and Krishna Keshav and Abhay Kumar and Faiz Akram},
        title = {Artificial Intelligence-Enabled Computer Vision for Construction Safety Monitoring and Management: A Systematic Literature Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11013-11028},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203162},
        abstract = {— Construction safety management remains a critical challenge in the industry, where traditional approaches often fail to address dynamic and complex hazards in real time. The recent emergence of artificial intelligence and computer vision technologies offers promising avenues for transforming safety practices. In this systematic literature review, we aim to synthesize and critically analyze the existing body of research on the application of AI and computer vision for construction safety management. Our objective is to identify key research dimensions, evaluate methodological approaches, and uncover gaps in the current literature. We conducted a rigorous, multi-stage review process that involved searching major academic databases, screening studies based on predefined inclusion criteria, and extracting data from selected articles. The analysis was guided by a structured framework that categorized findings into four dimensions: real-time hazard detection and monitoring, AI-driven risk management and strategic safety frameworks, integration with robotics and smart site systems, and other emerging themes. The results reveal that computer vision-based monitoring systems have been predominantly applied to detect unsafe behaviors and environmental hazards, while AI-driven frameworks increasingly support proactive risk assessment and decision-making. However, the integration of these technologies with Internet of Things and robotic systems remains underexplored, and many studies lack rigorous validation in real-world settings. We therefore conclude that future research should prioritize the development of holistic, scalable, and validated systems that combine vision-based detection with broader safety management frameworks. This review thus provides a comprehensive foundation for researchers and practitioners seeking to advance the role of AI and computer vision in construction safety.},
        keywords = {Construction Safety; Safety Management; Artificial Intelligence; Computer Vision; Literature Review},
        month = {May},
        }