SafetySafety aspects are mandatory for industrial-based shop floor workers and people handling rotating machinery. Ensuring worker safetySafety becomes tedious and has to be monitored to avoid workers, faculty, and lives. The Industry 4.0 standard provides industrial safetySafety and production monitoring with IoT, artificial intelligenceArtificial Intelligence, and cloud architectures. With the help of single-board computers and camera-based sensorsSensors optimally placed in shop floor locations, workers' safetySafety is monitored. Raspberry PiRaspberry Pi-based computers and Jetson nano-based computers monitor safetySafety features such as goggles, face masks, helmets, and their performance. The computing machines are trained with deep learningDeep Learning TensorFlow architecture, and computational time is analyzed. The machine is trained with training data sets, and incoming images from the RaspiCam sensorSensors are validated with the data set. The Raspberry PiRaspberry Pi and Jetson Nano provide 19 and 6 ms computing time, and the worker’s acknowledgment is given through firebase cloud architecture. The round-trip time delay for sending an acknowledgment to workers is 16 ms.


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    Titel :

    Machine Learning-Based IndustrialSafetyMonitoring System for Shop Floor Workers


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Sharma, Sanjay (Herausgeber:in) / Subudhi, Bidyadhar (Herausgeber:in) / Sahu, Umesh Kumar (Herausgeber:in) / Elangovan, Muniyandy (Autor:in) / Ahmed, N. Thoufik (Autor:in) / Arora, Sunil (Autor:in)

    Kongress:

    International Conference on Robotics, Control, Automation and Artificial Intelligence ; 2022 November 24, 2022 - November 26, 2022



    Erscheinungsdatum :

    2023-11-18


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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