College of Engineering

Image-based Classification of Air Pollution Using Different Pretrained CNN Models and A Small Dataset
Oct. 18, 2023, 11:47 a.m.

Objectives or Summary:

In this study, we demonstrate that utilizing several pretrained convolutional neural

network models, such as ResNet18, ResNet50, ResNet101, Mobilenetv2 and

Shufflenet is feasible to anticipate fine particulate matter (PM2.5) concentrations

with minimal computation time. The results show that, it is possible to estimate the

PM2.5 level through pretrained models using a small single scene dataset.

This is a seminar about our published paper on 2023-06-21