Urban Street Cleanliness Assessment Using Mobile Edge Computing and Deep Learning Python Django

Street cleaning is an important city service, which involves a set of activities concerning the cleanliness of the street (usually defined as pavements and adjoining edges of roads and grassed and planted areas) . Therefore, it involves street-sweeping (whether manual or machine), litter-picking, the uplift of fly-tipped refuse and the removal of graffiti and fly posting. When the street cleaning service is ineffective, the evidence is visible. And it could cause a significant impact on the quality of life and the attractiveness of its neighborhoods, towns and cities.Moreover, the effective street cleanliness could reduce the costs in cleaning underground water systems for cities. For this reason, researchers around the world are studying automated approaches, using a cleaning vehicle with cameras to capture the streets regularly and collect street information, such as street pictures, geographical location, date and time. Besides, existing object detection algorithms are used to detect images in the remote cloud platform. Finally, the detection results are sent to the city managers for decision making.