Publication: Computer-Vision Enabled Waste Management System for Green Environment
Type:
Article
Date
2021-12-09
Journal Title
Journal ISSN
Volume Title
Publisher
2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT
Abstract
Waste management has become a critical
requirement to maintain a green environment in Sri Lanka as
well as other countries. Town councils have to regularly collect
different types of wastes to clean cities/towns. Hence managing
the waste of the cities is a challenging task. However, most of the
urban councils currently use a manual approach to managing
waste. However, it results in many difficulties for the people and
cleaning staff who involve in the process by following strict
guidelines. Issues due to waste contamination, no proper
information management of waste collection, and no punctuality
in removing waste from the garbage bins are some of the
significant issues arising from the manual process. Due to the
drawbacks of the manual approach, social issues, environmental
issues, health issues can occur easily. This paper proposes a
better solution to replace this manual system with an automated
system to overcome these issues. Hence, the main objective of
this research is to introduce an ICT-based innovative design that
can be used to develop an effective waste management system in
town councils. In the proposed model, we will introduce a
Computer Vision-based smart waste bin system with real-time
monitoring that incorporates various technologies such as
computer vision, sensor-based IoT devices, and geographical
information system (GIS) related technologies. Our proposed
solution consists of a waste bin system, which is capable of
automated waste segregation. Our design facilitates the admin
users to expand the waste bin kit by adding more waste
categories in a user-friendly manner, making our product
adaptive in any environment. At the same time, waste bins can
notify the real-time waste status. Our system generates the
optimum collection routing path and displays it in a mobile app
using those real-time status details. We also demonstrate a lowcost
prototype.
Description
Keywords
Computer Vision, Machine learning, Convolutional Neural Networks, Unsupervised Clustering, Internet of Things, Cloud Computing, Green Environment
