Real-Time Personal Protective Equipment Detection using YOLOV8 Deep Learning Model
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Real-Time Personal Protective Equipment Detection using YOLOV8 Deep Learning Model
Mohammed Sameer1, M Pradeep2, A Prathish3, Dr.A.Vinoth Kumar4 Dr. T. Kumanan5, Dr. M.
Nisha6.
1,2,3 Students, Department of CSE
4,6 Assistant Professor, Department of CSE 5 Professor, Department of CSE
Dr.M.G.R Educational and Research Institute, Maduravoyal, Chennai 95, Tamilnadu, India
Abstract- Personal Protective Equipment (PPE) plays a vital role in ensuring worker safety in industries such as construction, manufacturing, healthcare, and mining. The absence or improper usage of PPE is one of the major reasons for workplace accidents and injuries. Manual monitoring of PPE compliance is time-consuming, error-prone, and highly dependent on human supervision. Hence, there is a strong need for an automated and intelligent PPE detection system. This paper presents a deep learning-based approach for automated detection of Personal Protective Equipment using the YOLOv8 object detection model. The proposed system detects essential PPE components such as helmets, masks, gloves, and safety vests from images and real-time video streams. Input images are preprocessed to enhance detection accuracy and are then passed to the YOLOv8 model for object localization and classification.Experimental evaluationdemonstrates that the proposed approach achieves high detection accuracy with real-time performance. The proposed system leverages the efficiency of the YOLOv8 object detection framework to monitor PPE usage in real-world environments. By processing images and video streams in a single forward pass, the model ensures fast and accurate identification of safety equipment. This approach is suitable for continuous surveillance applications where real-time response is required. The system can be deployed in industrial sites to assist safety officers in enforcingcompliance and reducing workplace risks.
Keywords:Personal Protective Equipment, YOLOv8, Object Detection, Deep Learning, Computer VisionIntroduction
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