Visual Pollution Detection Using Google Street View and YOLO

In recent years, visual pollution has become a major concern in rapidly rising cities. This research deals with detecting visual pollutants from the street images collected using Google Street View. For this experiment, we chose the streets of Dhaka, the capital city of Bangladesh, to build our image dataset, mainly because Dhaka was ranked recently as one the most polluted cities in the world. However, the methods shown in this study can be applied to images of any city around the world and would produce close to a similar output. Throughout this study, we tried to portray the possible utilisation of Google Street View in building datasets and how this data can be used to solve environmental pollution with the help of deep learning. The image dataset was created manually by taking screenshots from various angles of every street view with visual pollutants in the frame. The images were then manually annotated using CVAT and were fed into the model for training. For the detection, we have used the object detection model YOLOvS to detect all the visual pollutants present in the image. Finally, we evaluated the results achieved from this study and gave direction of using the outcome from this study in different domains.