利用高效的深度卷积神经网络通过智能手机拍摄的图像揭示局部聚合空气质量指数
Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network
AI总结：
本文提出一种高效的深度卷积神经网络（DCNN），通过智能手机拍摄的图像预测特定位置的PM2.5浓度和日均AQI指数，在达卡构建了首个公开的大气图像与PM2.5测量数据集，实验表明该模型参数更少且优于ViT、VGG19等流行模型。
摘要：
智能手机的普及性和移动性使其成为环境健康研究中广泛使用的工具。然而，在现有文献中，利用智能手机确定特定位置基于PM2.5浓度的聚合空气质量指数（AQI）的潜力在很大程度上仍未被探索。在本文中，我们深入研究了使用智能手机相机拍摄的图像预测特定位置PM2.5浓度所面临的挑战。由于严重的空气污染水平和大量暴露人口，我们的研究重点是孟加拉国首都达卡。我们的研究涉及开发一个深度卷积神经网络（DCNN），我们使用超过一千张拍摄并标注的室外图像对其进行训练。这些照片在达卡的各个地点拍摄，其标签基于从当地美国领事馆获取的PM2.5浓度数据，使用NowCast算法计算得出。通过监督学习，我们的模型在训练期间建立了相关性指数，增强了其作为基于图像的PM2.5浓度预测器（PPPC）的功能。这使算法能够从智能手机图像计算出等效的日均AQI指数。与流行的过度参数化模型不同，我们的模型显示出资源效率，因为它使用更少的参数。此外，测试结果表明，我们的模型在预测特定位置PM2.5浓度方面优于ViT和INN等流行模型，以及VGG19、ResNet50和MobileNetV2等流行的基于CNN的模型。我们的数据集是首个公开可用的集合，包括达卡的大气图像和相应的PM2.5测量值。我们的代码和数据集可在https://github.com/lepotatoguy/aqi获取。
英文摘要：
The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our codes and dataset are available at https://github.com/lepotatoguy/aqi.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
