Multilevel Feature Fusion and Spatial Attention-Based Deep Learning Models for Personal Protective Equipment Detection
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DOI:
https://doi.org/10.32523/bulmathenu.2025/2.2Keywords:
deep learning, personal protective equipment, industrial safety, intelligent system, computer visionAbstract
The proposed research work examines the effectiveness of deep learning models for the automatic recognition of personal protective equipment (PPE) in complex environments encountered in various industrial workplaces. Since PPE is designed to protect workers from multiple injuries and hazards. Monitoring their use using state-of-the-art deep learning models is an important solution. Given the diversity of these hazards, a state-of-the-art but unexplored multi-class dataset containing diverse scenes and fully annotated was trained on the Yolov11 deep learning network to evaluate their robustness and potential in real-world applications. The results show that the architecturally improved models can effectively handle complex and imbalanced data. In addition, the study conducted a comparative analysis with YOLOv8 and YOLOv10 models and performed an analysis for each architecture variant. This research contributes to the understanding of the topic by providing new insights and perspectives on the development of intelligent systems in the field of industrial safety, offering a practical solution to monitor workers' compliance with safety rules in the workplace and thereby saving lives.
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