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About Urban Street Cleanliness Assessment Using Mobile Edge Computing and Deep Learning
This document proposes a new approach for assessing urban street cleanliness using mobile edge computing and deep learning. High-resolution cameras on vehicles will collect street images, which will be temporarily stored and processed by edge servers. The processed data will then be transmitted to a cloud data center for analysis. A deep learning model called Faster R-CNN will identify street garbage categories and count the amount of garbage. The results will be used to assess street cleanliness levels, which will help city managers efficiently arrange clean-up crews. The approach is demonstrated using street images from Jiangning District, Nanjing, China.
Detailed Information
| Author: | ['ruler3382'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 13 |
| Language: | English |
| Format: | |
| Price: | FREE |
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