用手机拍照实时测空气质量，精度高还省数据增强
Can Deep Learning Trigger Alerts from Mobile-Captured Images?
- 用卷积神经网络建模，直接从照片预测污染浓度
- 2种污染物误差仅0.0077，5种为0.0112，优于现有模型
- 无需数据增强也能保持精度，适合普通用户日常使用
本研究提出一种基于移动摄像头图像的实时空气质量评估与推荐方法。构建了回归型卷积神经网络模型，通过挖掘输出参数间的内在关系，专门优化用于空气污染预测。在2种和5种污染物预测上，均方误差分别为0.0077和0.0112，优于现有模型。同时验证了数据增强的有效性，实验表明原始数据与增强数据在准确率上差异极小。此外，开发了实时、易用的可视化仪表盘，可动态显示由手机拍摄图像推算出的空气质量指数及污染物值，并结合用户健康状况推荐是否适宜前往某地。该系统验证了数据增强的必要性，推动了基于CNN的空气质量回归建模，并实现以用户为中心的移动端环境健康监测。整体方案为个人做出科学环保决策提供实用支持。
原文摘要 · Abstract (English)
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.
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