Lightweight Multi-PPE Detection for Edge-Based Industrial Safety Monitoring
Main Article Content
Abstract
Personal Protective Equipment (PPE) is a key safety issue in the workplace. These are settings that are usually accompanied with complicated back- grounds, occlusions and huge disparities in the object sizes and so detection is not an easy task. Conventional vision based PPE detection systems are often not very resilient in such dynamic conditions. To remedy these issues, this paper will suggest a high-performance PPE detection model using YOLOv8 in real-time industrial safety monitoring. A stimulated multi-class PPE dataset is trained in the proposed system within a supervised learning strategy. The model carries out precise localization and classification by modeling bounding box regression and classification. The training procedure reduces bounding box loss, classification loss and distribution focal loss in order to improve detection. Experimental findings illustrate that the model has a detection error of 96.2, which depicts a great potential of detecting and locating the PPE components with great precision.
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