Introducing AI in garment fault detection using YOLOv5 to reduce bottleneck
Abstract
In order to reduce manpower and bottleneck in the inspection system of industrial
garments, we explored the application of the YOLOv5 model using our very own
dataset of defective clothing pieces. The economy of Bangladesh heavily depends
on the garment industry. However, in this day and age of advanced technology, it
is getting harder to have efficient manpower in the garment industry. Motivated
to solve this problem, we decided to devise ways to explore AI implementations,
particularly in the Bangladeshi garment industry system. Since there is no existing
and efficient defective garment dataset specifically for our desired research work so
we created our own dataset. This dataset has a total of 2,525 images and 7 different
classes. By thoroughly analyzing our data from pre-processing to its performance
after the application in the YOLOv5 model, we have tried to create a useful dataset.
The models have achieved a good mean average precision across all 7 classes. Our
research has only scratched a small surface of an area of interest where advanced AI
and machine learning technologies can bring a lot more advancement.