Histogram of intensity feature extraction for automatic plastic bottle recycling system using machine vision

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Currently, many recycling activities adopt manual sorting for plastic recycling that relies on plant personnel who visually identify and pick plastic bottles as they travel along the conveyor belt. These bottles are then sorted into the respective containers. Manual sorting may not be a suitable option for recycling facilities of high throughput. It has also been noted that the high turnover among sorting line workers had caused difficulties in achieving consistency in the plastic separation process. As a result, an intelligent system for automated sorting is greatly needed to replace manual sorting system. The core components of machine vision for this intelligent sorting system is the image recognition and classification. In this research, the overall plastic bottle sorting system is described. Additionally, the feature extraction algorithm used is discussed in detail since it is the core component of the overall system that determines the success rate. The performance of the proposed feature extractions were evaluated in terms of classification accuracy and result obtained showed an accuracy of more than 80%.

Original languageEnglish
Pages (from-to)583-588
Number of pages6
JournalAmerican Journal of Environmental Sciences
Volume4
Issue number6
Publication statusPublished - 2008

Fingerprint

Plastic bottles
histogram
Sorting
sorting
Computer vision
Recycling
Feature extraction
recycling
plastic
Image recognition
Image classification
Bottles
Intelligent systems
Containers
turnover
Throughput
Personnel
Plastics

Keywords

  • Bounding box image
  • Histogram of intensity
  • Plastic recycling
  • Region of interest

ASJC Scopus subject areas

  • Pollution
  • Ecology

Cite this

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title = "Histogram of intensity feature extraction for automatic plastic bottle recycling system using machine vision",
abstract = "Currently, many recycling activities adopt manual sorting for plastic recycling that relies on plant personnel who visually identify and pick plastic bottles as they travel along the conveyor belt. These bottles are then sorted into the respective containers. Manual sorting may not be a suitable option for recycling facilities of high throughput. It has also been noted that the high turnover among sorting line workers had caused difficulties in achieving consistency in the plastic separation process. As a result, an intelligent system for automated sorting is greatly needed to replace manual sorting system. The core components of machine vision for this intelligent sorting system is the image recognition and classification. In this research, the overall plastic bottle sorting system is described. Additionally, the feature extraction algorithm used is discussed in detail since it is the core component of the overall system that determines the success rate. The performance of the proposed feature extractions were evaluated in terms of classification accuracy and result obtained showed an accuracy of more than 80{\%}.",
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AU - Ramli, Suzaimah

AU - Mustafa, Mohd. Marzuki

AU - Hussain, Aini

AU - Abd. Wahab, Dzuraidah

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