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Modernizing and Validating Safety Product

  • 2025
  • OriginalPaper
  • Chapter
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Abstract

This chapter delves into the development and implementation of a combined safety system that leverages facial recognition and other advanced technologies to bolster security and safety measures. The paper emphasizes the refinement of face recognition algorithms and machine learning models to enhance their precision and efficiency. Real-world testing is crucial for ensuring the effectiveness and reliability of these developments, which are integral to the integration of safety-related products. The system prioritizes user-friendly interfaces to streamline management and operation processes, facilitating smooth implementation and usage. Key areas of focus include fire and smoke detection, trespassing detection, and safety gear detection. The paper discusses various datasets and machine learning models, such as YOLOv5, YOLOv7, YOLOv8, and Faster R-CNN, comparing their performance metrics for fire and smoke detection. The proposed architecture includes a centralized dashboard for administrators, offering features like real-time notifications, compliance tracking, and historical data access. The conclusion highlights the potential of the system to revolutionize safety and security across different sectors, emphasizing the need for pilot projects and comprehensive evaluations for successful implementation.

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Title
Modernizing and Validating Safety Product
Authors
Neha Agarwal
Monika Mangla
Richa Sharma
Sharvari Patil
Priyanca Gonsalves
Copyright Year
2025
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-96-9975-9_12
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