Get-AQI in One shot-4 (GAOs-4)
Description
To make the experimental results more accurate and the model more generalizable, we propose a new dataset GAOs-4. We used cell phone photography to manually collect 5700 environmental images at various times of the day in the Beijing area as our air quality dataset. When collecting images, we try to avoid the interference of external factors on the images. For example, avoid capturing data in bad weather and low light, avoid direct sunlight to affect the representation of air quality in images, etc. The time, location, and AQI value of the acquisition are recorded at the same time as the image is acquired. The images were then divided into six categories based on the correspondence between AQI and air quality classes in Table II. To improve the quality of the dataset and enhance the classification performance of the network, we carefully filter the collected dataset. We removed images that were blurred due to improper photography, were dim due to backlighting, and images where the sky and buildings were covered by nearby objects. We continuously filter and adjust the captured images to remove the redundant images with good air quality so that the images in each category are evenly distributed. We ended up with a high-quality environmental image dataset containing 3700 images. We divided them into 2960 training images and 740 validation images.