Safety helmet wearing detection in workers images using the Bag of Visual Words (BoVW) method
DOI:
https://doi.org/10.30973/progmat/2023.15.3/2Keywords:
Personal protective equipment, Bag of visual words, Supervised learning, Computer visionAbstract
The safety helmet is an essential personal protective equipment to prevent fatal accidents in factories and building sites. The problem is that many places do not perform proper inspections, particularly when this task is done by people. However, automatic detection of objects through computer vision systems with low cost cameras and artificial intelligence algorithms such as the Bag of Visual Word (BoVW) method are a suitable option to inspect at the access control point that workers and occasional visitors wear safety helmets before entering hazardous areas. In this paper we report that the training stage was performed with experimental images arranged into two classes, obtained after applying an edge detection filter and a previous reduction of their original size. As a result, the average training time was reduced to 11.9 seconds and a 95.8% accuracy was achieved. The test stage was carried out with images downloaded from the internet, the average testing time was reduced to 0.63 seconds and an 88.3% accuracy was achieved. It shows that the bag of visual words method has a good performance on the speed and accuracy of the safety helmet detection task.
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