Nanomaterial-Enhanced Robotic System for Real-Time Crowd, Violence, Safety Detection Using YOLOv8 in oil and chemical Industry
DOI:
https://doi.org/10.62486/978-9915-9851-0-7_202633Keywords:
YOLOv8, Real-Time Surveillance, Crowd and violence detection, oil and chemical Industry, Computer Vision, Robotic Systems, Nanomaterials in roboticsAbstract
Quick and accurate recognition of suspicious and abnormal density variations and violent acts is necessary to ensure the safety of the population in high-density and sensitive areas. This publication introduces a nanomaterial-crowded robotic surveillance system, which combines the improved detectives of small-scale materials with the YOLOv8 deep learning framework in terms of detecting crowds and violence in real time. The nanomaterial-based sensors enhance the responsiveness of the robotic system and make it sensitive to imaging, noise-sensitive when acquiring signals and more adaptable to its environmental capacity. Live video streams are targeted by the YOLOv8 processor and suspicious activities are detected with high accuracy, and the robotic platform will automatically cause audible alarms and send email notifications to the relevant authorities. The proposed system will overcome the drawbacks of manual monitoring, where there is increased accuracy in detection, faster reaction, and scalability of the operations. Experimental testing of various real-world systems proves that the integration of nanomaterial-based sensor technology with the contemporary computer vision can greatly improve the reliability of the system, and it is a proactive and efficient solution to the automation of the public safety and also safety enviroment in oil and chemical Industry.
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Copyright (c) 2026 Dr.Kamaleshwar T, Lokesh Kumar Paila, Sneja Godi, Christo Ananth, V.H. Abdullayev, R.G. Abaszade (Author)
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