Application development for plastic bottle detection using deep learning
Nowadays, recycling centers still rely on human workers which is low efficiency and working environment is bad for the human workers. Hence, deep learning is introduced to detect the plastic bottles on the moving conveyer belt in the recycling centers. In this project, three pre-trained deep learn...
| Main Author: | |
|---|---|
| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2024
|
| Subjects: | |
| Online Access: | http://eprints.utar.edu.my/6566/ http://eprints.utar.edu.my/6566/1/SE_2002937_FYP_Report_%2D_FongYunXin_FONG_YUN_XIN.pdf |
| _version_ | 1848886715406614528 |
|---|---|
| author | Fong, Yun Xin |
| author_facet | Fong, Yun Xin |
| author_sort | Fong, Yun Xin |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | Nowadays, recycling centers still rely on human workers which is low efficiency and working environment is bad for the human workers. Hence, deep learning is introduced to detect the plastic bottles on the moving conveyer belt
in the recycling centers. In this project, three pre-trained deep learning models is selected to train and detect the plastic bottles. The three selected pre-trained deep learning models are YOLOv8, Faster R-CNN and SSD. The results show that YOLOv8 achieved the highest mean average precision for the custom dataset which is 0.923 compared to Faster RCNN and SSD. Thus, YOLOv8 is selected and further tested with the real video from the recycling center to detect the plastic bottles on the conveyer belt. In the video, YOLOv8 achieved an average precision of 0.3026 in detecting the plastic bottles, but the average precision significantly improved to 0.6783 when the waste products is less overlapping on the moving conveyer belt. The application had passed the user satisfactory survey and user acceptance test, so it is easy to be used for people who does not have knowledge in deep learning.
|
| first_indexed | 2025-11-15T19:42:54Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-6566 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:42:54Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-65662024-07-09T08:24:37Z Application development for plastic bottle detection using deep learning Fong, Yun Xin QA75 Electronic computers. Computer science QA76 Computer software Nowadays, recycling centers still rely on human workers which is low efficiency and working environment is bad for the human workers. Hence, deep learning is introduced to detect the plastic bottles on the moving conveyer belt in the recycling centers. In this project, three pre-trained deep learning models is selected to train and detect the plastic bottles. The three selected pre-trained deep learning models are YOLOv8, Faster R-CNN and SSD. The results show that YOLOv8 achieved the highest mean average precision for the custom dataset which is 0.923 compared to Faster RCNN and SSD. Thus, YOLOv8 is selected and further tested with the real video from the recycling center to detect the plastic bottles on the conveyer belt. In the video, YOLOv8 achieved an average precision of 0.3026 in detecting the plastic bottles, but the average precision significantly improved to 0.6783 when the waste products is less overlapping on the moving conveyer belt. The application had passed the user satisfactory survey and user acceptance test, so it is easy to be used for people who does not have knowledge in deep learning. 2024 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6566/1/SE_2002937_FYP_Report_%2D_FongYunXin_FONG_YUN_XIN.pdf Fong, Yun Xin (2024) Application development for plastic bottle detection using deep learning. Final Year Project, UTAR. http://eprints.utar.edu.my/6566/ |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software Fong, Yun Xin Application development for plastic bottle detection using deep learning |
| title | Application development for plastic bottle detection using deep learning |
| title_full | Application development for plastic bottle detection using deep learning |
| title_fullStr | Application development for plastic bottle detection using deep learning |
| title_full_unstemmed | Application development for plastic bottle detection using deep learning |
| title_short | Application development for plastic bottle detection using deep learning |
| title_sort | application development for plastic bottle detection using deep learning |
| topic | QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://eprints.utar.edu.my/6566/ http://eprints.utar.edu.my/6566/1/SE_2002937_FYP_Report_%2D_FongYunXin_FONG_YUN_XIN.pdf |