Abstract: During the process of smart city construction, city planners and managers always spend a lot of energy and money
for cleaning street garbage due to the random appearances of street garbage. Consequently, visual street cleanliness
assessment is particularly important. However, the existing assessment approaches have some clear disadvantages, such as
the collection of street garbage information is not automated and street cleanliness information is not real-time. To address
these disadvantages, this paper proposes a novel urban street cleanliness assessment approach using mobile edge computing
and deep learning. First, the high resolution cameras installed on vehicles collect the street images. Mobile edge servers are
used to store and extract street image information temporarily. Second, these processed street data is transmitted to the cloud
data centre for analysis through city networks. At the same time, Faster Region-Convolutional Neural Network (Faster RCNN)
is used to identify the street garbage categories and count the number of garbage. Finally, the results are incorporated
into the street cleanliness calculation framework to ultimately visualize the street cleanliness levels, which provides
convenience for city managers to arrange clean-up personnel effectively.
Keywords: Smart cities, street cleaning, garbage detection, deep learning, mobile edge computing