| 总结: | Multiple studies conducted by Singapore’s Ministry of Manpower highlight the vast number
of injuries occurring in industrial workplaces annually. Despite the existence of laws designed
to reduce injuries by enforcing the usage of personal protective equipment (PPE), there is still
a significant risk of workplace accidents due to non-compliance from workers. With the recent
advancement in efficient object detection models and the widespread utilisation of surveillance
cameras in workplaces, this study proposes the development and implementation of an accurate
and efficient real-time PPE detection system.
Through comprehensive research and comparison analysis conducted on various object
detection models, YOLOv8 was streamlined to be utilised as the baseline model due to its
accuracy and advantages in inference speed. Additionally, the expansion of a pre-existing PPE
dataset to increase the total number of samples from 9,886 to 12,981 images and the number
of classes from 11 to 12 classes was carried out to improve the detection model’s ability to
generalise unseen data with more efficiency. With various data pre-processing and
augmentation strategies explored to refine the overall performance of the detection model, a
PPE detection system utilising the YOLOv8 model was achieved with a mean Average
Precision at 0.5 intersection over union threshold (mAP@0.5) of 93.1 % along with an
inference speed of 19.4 milliseconds (ms).
|
|---|