Abstract:
Climate change is one of the hardest problems humanity will have to face
in the next century. Data analysis and computer vision are two powerful
tools that can help us perform tasks that would usually take more time
and resources to finish. Therefore, monitoring air quality, especially in
developing countries should be the first step to save the environment.
Measurement of air quality is a task that, currently needs the help of
specialized equipment and infrastructure. These equipments are either
very costly or require skills to operate or both making it difficult to
provide air quality information at remote locations or at desired spots
even in cities. In this study, we have tried to measure the air quality
through images which can be taken using a normal camera. For this
purpose, we used deep learning techniques, where we trained ResNet18
using a public image database. Performance is evaluated by plotting
confusion matrix. We also measure precision, recall, F1-score and
accuracy. Results are analyzed by plotting ROC curve and precision-recall
curve.
