<![CDATA[Due to the inherently hazardous nature of operations in the oil and gas industry, strict compliance with safety protocols, including the obligatory use of Personal Protective Equipment (PPE), is essential for all workers. Nonetheless, monitoring PPE compliance through manual observation remains inefficient and prone to errors, especially in expansive and intricate work settings. To address this challenge, there is a growing demand for an intelligent system capable of detecting PPE usage accurately and instantaneously. This research introduces a Computer Vision approach employing Convolutional Neural Networks (CNNs) to identify PPE usage among workers within oil and gas environments. The system leverages a comprehensive dataset comprising images of workers equipped with different types of PPE, such as helmets, safety vests, and face masks. These images are used to train a CNN model designed to distinguish and classify the safety equipment. Experimental results demonstrate that the proposed CNN model achieves an impressive detection accuracy of 94.2% on validation data and maintains reliable performance across varying lighting and camera angles. Moreover, the system is capable of identifying PPE violations in under one second per frame, making it suitable for real-time surveillance applications. As a result, this solution offers a promising enhancement to workplace safety oversight, with the potential to markedly reduce accident rates in the industry. The findings also pave the way for future integration with IoT-based monitoring platforms and further refinement of model adaptability across diverse industrial scenarios. The primary innovation of this study lies in the optimized deployment of CNNs tailored to the challenging conditions of oil and gas sites, delivering high detection precision and rapid response times—an area that has seen limited exploration in existing literature.]]>
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