<![CDATA[Occupational Safety and Health (OSH) is a critical aspect in high-risk work environments, where the consistent use of Personal Protective Equipment (PPE) plays a vital role in preventing workplace accidents. However, non-compliance with PPE regulations remains a significant issue, contributing to a high number of work-related injuries in Indonesia. This study proposes an automated detection and classification system for PPE usage, specifically helmets and vests, using the Backpropagation algorithm in artificial neural networks. A total of 100 images were utilized, equally divided between complete and incomplete PPE usage. The dataset was split into 60% training and 40% testing. Image segmentation was performed using HSV color space conversion and thresholding, followed by RGB color feature extraction. The Backpropagation algorithm was then employed for classification. Experimental results show an average accuracy of 90%, with precision, recall, and F-measure all reaching 0.9. Despite some misclassifications due to color similarity between helmets and head coverings, the model demonstrated robust performance with relatively low computational requirements. This study contributes to the field of computer vision and intelligent safety systems by demonstrating the practical effectiveness of lightweight ANN architectures for PPE detection in real-time industrial scenarios, thereby highlighting the potential of backpropagation as an adaptive and practical alternative to more complex deep learning approaches for real-time PPE detection in occupational safety monitoring systems.]]>
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