The real-time information on surrounding air quality index (AQI) is important for the public to protect themselves from air pollution. Traditional methods have some shortages regarding the estimation time and running efficiency. Consequently, the AQI results cannot meet the needs of personal protection and environmental management. With the popularity of smart terminals, it is easier to collect particular environmental images for AQI estimation tasks. Therefore, a real-time and image-based deep learning model named YOLO-AQI is proposed. Based on the object detection algorithms, the model has better performance regarding the AQI estimation speed. By optimizing the parameter transfer and network structure, the model takes an average of 0.0582 s to perform feature analysis and achieves 75.15% accuracy on AQI estimation tasks. Comparing YOLO-AQI with several image recognition models (VGG, AlexNet, GoogLeNet, MobileNet, and ResNet), it shows that YOLO-AQI outperforms other models by 14.8% on accuracy and by 71.1% on running speed. This method can provide real-time AQI level information for remote areas such as rural.

中文翻译：

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基于深度学习的实时和基于图像的 AQI 估计

周围空气质量指数（AQI）的实时信息对于公众保护自己免受空气污染非常重要。传统方法在估计时间和运行效率方面存在一些不足。因此，AQI结果不能满足个人防护和环境管理的需要。随着智能终端的普及，为 AQI 估计任务收集特定的环境图像变得更加容易。因此，提出了一种基于图像的实时深度学习模型 YOLO-AQI。基于目标检测算法，该模型在 AQI 估计速度方面具有更好的性能。通过优化参数传递和网络结构，模型进行特征分析平均耗时0.0582 s，在AQI估计任务上的准确率达到75.15%。将 YOLO-AQI 与几种图像识别模型（VGG、AlexNet、GoogLeNet、MobileNet 和 ResNet）进行比较，表明 YOLO-AQI 在准确率上优于其他模型 14.8%，在运行速度上优于其他模型 71.1%。这种方法可以为农村等偏远地区提供实时的AQI水平信息。