• Shreyas suresh likhar
• Vivek Tripathi

The primary objective of this paper is to develop a robust ML model capable of accurately identifying whether workers are wearing appropriate Persona Protective Equipment, including helmets, goggles, vests, gloves, and boots. The proposed system utilizes computer vision algorithms trained on a dataset of annotated images to recognize and classify different types of PPE. Additionally, the model incorporates real-time video processing to enable continuous monitoring of PPE compliance in dynamic work environments. Key components of the project include data collection, preprocessing, model training, and evaluation. Annotated images of workers wearing various PPE configurations are collected to construct a comprehensive dataset for training and testing purposes. Image preprocessing techniques are applied to enhance the quality and consistency of the data. Several ML algorithms, including you only look once (YOLO), are trained on the dataset to learn the visual features associated with different PPE items

"DETECTING PERSONAL PROTECTIVE EQUIPMENT USING YOLO", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.l94-l102, April-2024, Available :http://www.jetir.org/papers/JETIR2404B12.pdf

"DETECTING PERSONAL PROTECTIVE EQUIPMENT USING YOLO", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppl94-l102, April-2024, Available at : http://www.jetir.org/papers/JETIR2404B12.pdf