2019
DOI: 10.1109/access.2019.2914270
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Urban Street Cleanliness Assessment Using Mobile Edge Computing and Deep Learning

Abstract: During the process of smart city construction, city 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 ur… Show more

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Cited by 40 publications
(12 citation statements)
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References 30 publications
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“…Edge computing has caused a sensation in smart cities in recent years due to its many IoT-based applications. Unlike the previous centralized vision, edge computing proposes a new decentralized approach that can seize opportunities and deal with the harm caused by urban transformation [ 19 ]. Edge computing allows real-time processing and analysis of large amounts of complex data on the device itself (rather than a large data center).…”
Section: Recent Workmentioning
confidence: 99%
“…Edge computing has caused a sensation in smart cities in recent years due to its many IoT-based applications. Unlike the previous centralized vision, edge computing proposes a new decentralized approach that can seize opportunities and deal with the harm caused by urban transformation [ 19 ]. Edge computing allows real-time processing and analysis of large amounts of complex data on the device itself (rather than a large data center).…”
Section: Recent Workmentioning
confidence: 99%
“…The scale and urgency of this problem necessitate the use of technology for a scalable and safe solution. Using mobile edge computing and installing high-resolution cameras on garbage trucks and other municipality vehicles, street cleaning can be performed more efficiently and faster [ 61 ]. Therefore, in this research a solution for addressing these issues is presented.…”
Section: Litter Management In Smart Eco-cyber-physical Systemsmentioning
confidence: 99%
“…Literature [21] uses a self-encoding network to reconstruct the garbage classification data set and uses CNN to automatically extract features from the data set. Zhang et al [22] used the Faster RCNN algorithm to detect 681 street pictures with 9 categories of garbage targets, and the detection mAP was 0.82, but there was a problem of unbalanced categories. Seredkin et al [23] used Faster RCNN network to perform garbage classification which has high accuracy and effectively realized garbage identification.…”
Section: Related Workmentioning
confidence: 99%