Can Deep Learning Trigger Alerts from Mobile-Captured Images?

TL;DR

This paper introduces a CNN-based system that uses mobile camera images for real-time air quality assessment, demonstrating minimal accuracy loss with data augmentation and providing user-friendly health recommendations.

Contribution

It develops a regression CNN model tailored for air quality prediction and verifies the effectiveness of data augmentation techniques in this context.

Findings

CNN model achieves low MSE of 0.0077 and 0.0112 for pollutants

Data augmentation shows minimal impact on accuracy

Real-time dashboard provides actionable air quality info

Abstract

Our research presents a comprehensive approach to leveraging mobile camera image data for real-time air quality assessment and recommendation. We develop a regression-based Convolutional Neural Network model and tailor it explicitly for air quality prediction by exploiting the inherent relationship between output parameters. As a result, the Mean Squared Error of 0.0077 and 0.0112 obtained for 2 and 5 pollutants respectively outperforms existing models. Furthermore, we aim to verify the common practice of augmenting the original dataset with a view to introducing more variation in the training phase. It is one of our most significant contributions that our experimental results demonstrate minimal accuracy differences between the original and augmented datasets. Finally, a real-time, user-friendly dashboard is implemented which dynamically displays the Air Quality Index and pollutant values derived from captured mobile camera images. Users' health conditions are considered to recommend whether a location is suitable based on current air quality metrics. Overall, this research contributes to verification of data augmentation techniques, CNN-based regression modelling for air quality prediction, and user-centric air quality monitoring through mobile technology. The proposed system offers practical solutions for individuals to make informed environmental health and well-being decisions.