Machine vision based recyclable solid waste sorting system
Also available in printed version
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| Other Authors: | |
| Format: | Bachelor thesis |
| Language: | English |
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Universiti Teknologi Malaysia
2025
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| Online Access: | https://utmik.utm.my/handle/123456789/52839 |
| Abstract | Abstract here |
| _version_ | 1854975114737614848 |
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| author | Chai, Wei Chee |
| author2 | Usman Ullah Sheikh, supervisor |
| author_facet | Usman Ullah Sheikh, supervisor Chai, Wei Chee |
| author_sort | Chai, Wei Chee |
| description | Also available in printed version |
| format | Bachelor thesis |
| id | utm-123456789-52839 |
| institution | Universiti Teknologi Malaysia |
| language | English |
| publishDate | 2025 |
| publisher | Universiti Teknologi Malaysia |
| record_format | dspace |
| record_pdf | Abstract |
| spelling | utm-123456789-528392025-08-21T13:28:51Z Machine vision based recyclable solid waste sorting system Chai, Wei Chee Usman Ullah Sheikh, supervisor Electrical engineering Also available in printed version Recycle and reuse of solid waste has become one hot issue in Malaysia as many of the landfill sites are approaching or already exceeded their maximum capacity. Furthermore, Malaysia government announced to implement a new Act 672 which is to encourage all Malaysian households to separate their solid wastes according to their types. Thus, this project proposes a machine vision based sorting system to assist human workers in sorting the recyclable solid wastes in the recyclable center which will be receiving tons of solid wastes in the future. In this system, the collected crumpled recyclable solid waste will be presented on the conveyor belt individually. The camera located on top of the conveyor belt will capture its image and send to the computer for image preprocessing. Next, the processed image will be fed into a pretrained deep learning Convolutional Neural Network (CNN) for recognition process to identify its class. The CNN used for recognition process was pre-trained using 120 different sample images of each targeted crumpled recyclable solid wastes consisting of aluminum can, plastic bottle and paper. After recognition process, the computer will send the recognition result to Arduino Uno R3 which is used to control the servo motor of the rotated-bin using serial communication. The aim of this final year project is to use machine vision and CNN to recognize and sort the targeted crumpled recyclable solid wastes into their respective bins. The proposed system achieved recognition accuracy of 90%, 83.33% and 63.33% for crumpled paper, crumpled plastic bottle and crumpled aluminum can respectively zulaihi UTM 73 p. Project Paper (Sarjana Muda Kejuruteraan (Elektrik - Elektronik)) - Universiti Teknologi Malaysia, 2016 2025-03-14T07:39:40Z 2025-03-14T07:39:40Z 2016 Bachelor thesis https://utmik.utm.my/handle/123456789/52839 vital:112944 valet-20180724-08130 ENG Closed Access UTM Complete Unpublished Completion application/pdf Universiti Teknologi Malaysia |
| spellingShingle | Electrical engineering Chai, Wei Chee Machine vision based recyclable solid waste sorting system |
| thesis_level | Other |
| title | Machine vision based recyclable solid waste sorting system |
| title_full | Machine vision based recyclable solid waste sorting system |
| title_fullStr | Machine vision based recyclable solid waste sorting system |
| title_full_unstemmed | Machine vision based recyclable solid waste sorting system |
| title_short | Machine vision based recyclable solid waste sorting system |
| title_sort | machine vision based recyclable solid waste sorting system |
| topic | Electrical engineering |
| url | https://utmik.utm.my/handle/123456789/52839 |
| work_keys_str_mv | AT chaiweichee machinevisionbasedrecyclablesolidwastesortingsystem |