A Robust deep learning architecture for FireFighter PPEs detection

Personal Protective Equipment (PPE) is one of the
primary defence mechanisms to reduce the exposure of the
personnel to hazardous environments. It’s significantly important
to Fire Fighters as they are constantly exposed to dangerous
elements such as fire, gas or chemicals. Unfortunately, in real-
time emergencies, such as fires, it is very difficult to identify if a
responder using PPE is fully equipped to reduce any accidents
in the workplace or even coordinate response actions due to the
high pace of the situation. A lack of a unified Fire Fighting PPE
image dataset was also observed, which makes the task of training
Machine Learning (ML) models to solve this problem a challenge.
To that end, we first create a general purpose FireFighter
Equipment Detection dataset. We then propose to utilise the
widely used YoloV5 Deep Network architecture to detect different
PPE components in real-time. This work leverages the pre-
trained YoloV5 model, using transfer learning to fine-tune the
model using the created detection dataset that contains targeted
Fire Fighter PPE images. By employing the pre-trained model
which requires substantially fewer training samples, we were able
to achieve a considerably good performance on the Fire Fighter
PPE object detection. The proposed method can distinguish four
different PPE components such as a Helmet, Gloves, Mask or
Insulated protective cloth, achieving high detection efficiency
which is experimentally established.