A Comprehensive Review of Geo-Verified and AI-Assisted Frameworks for Fire Safety NOC Inspections
Sachinkumar Gupta , Omkar Patil, Kunal Jain , Dr. Harshali Patil
Department of Computer Engineering, Thakur College Of Engineering And Technology, Mumbai, India
sachin.apwig@gmail.com
Department of Computer Engineering, Thakur College Of Engineering And Technology, Mumbai, India
1032231333@tcetmumbai.in
Department of Computer Engineering, Thakur College Of Engineering And Technology, Mumbai, India
kunaljain0809@gmail.com
Department of Computer Engineering, Thakur College Of Engineering And Technology, Mumbai, India
harshali.patil@thakureducation.org
Abstract - Verification and assurance of fire safety compliance in buildings has historically been regarded as a very manual, slow, and error-prone process. Manually inspection is time taking and requires a lots of effort, which can delay in issuance of the No-Objection Certificates (NOCs), compromising public safety.Innovative Solution: The authors present the FireNOC AI-Powered Safety Platform, a revolutionary, cross-platform, system that will completely transform the entire Fire Safety NOC issuance cycle. The biggest advancement for this platform is a very special amalgamation of various AI technologies that allows for a comprehensive, data-driven inspection process. The application will cleverly integrate the combination of facial recognition for safe inspector verification, OCR for quick device verification, smart acoustic analysis for fire alarm verification, and advanced computer vision for critical area verification (such as smoke detectors, emergency lights, and sprinkler systems). The paper details the system architecture, where it uses technologies, such as TensorFlow.js and the Google Vision API for an AI-summarized report to be human approved at the end. The platform automation of data collection and review directed by FireNOC is intended to make fire safety inspections distinctly more accurate, efficient, and transparent, to which public safety can be notably enhanced through a technological intervention.
Keywords: Fire Safety, Artificial Intelligence, Computer Vision, Automated Inspection, NOC, Machine Learning, Compliance Verification
