| Summary: | Every year, countless factory accidents occur in various countries. One of the
important causes of workplace accidents is the inadvertent wearing of personal
protective equipment (PPE) or the incomplete wearing of PPE. Therefore, addressing
the necessity and importance of designing a smart system that can
automatically monitor the integrity of PPE worn by workers in industrial environments.
In this dissertation, Deep Learning (DL) framework-based YOLOv5
detection method is implemented to realize PPE detection, including safety helmets,
goggles, masks, reflective clothes, and gloves. In the fusion of prelabeled
datasets and self-labeled datasets, the detection objects are classified into 10 categories.
Furthermore, evaluation metrics such as mean average precision (mAP),
recall rate, and confusion metrics are used to realize a multi-faceted assessment
of detection performance. With the number of 2923 images, the 10 classes
mAP of this system reaches 82.6%, and the mAP of ”no goggles” and ”mask”
achieves the highest which is 99.5%. In addition, this system can also be used
in other occasions that have the same requirements for detection, such as hospitals,
to ensure the personal safety of doctors and patients and avoid virus
infection.
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