Domestic garbage target detection based on improved YOLOv5 algorithm
The output of household garbage has increased rapidly in the world, due to the development of the economy, the improvement of the living standards of residents, and the acceleration of urbanization. The process of manual garbage classification is time-consuming and laborious, and the effect is still...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
IEEE
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/37565/ |
| _version_ | 1848848641156972544 |
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| author | Ma, Haohao Wu, Xuping As'arry, Azizan Han, Weiliang Mu, Tong Feng, Yanwei |
| author_facet | Ma, Haohao Wu, Xuping As'arry, Azizan Han, Weiliang Mu, Tong Feng, Yanwei |
| author_sort | Ma, Haohao |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The output of household garbage has increased rapidly in the world, due to the development of the economy, the improvement of the living standards of residents, and the acceleration of urbanization. The process of manual garbage classification is time-consuming and laborious, and the effect is still not satisfactory. In order to reduce the intensity of manual garbage classification and improve the efficiency and accuracy of garbage classification, a new type of household garbage classification based on improved YOLOv5 algorithm visual recognition is designed. Make a data set for garbage detection, and after training on the improved YOLOv5 network framework, detect the status of garbage in real time. Experiments have proved that the accuracy of intelligent classification reached 98.27%, which is 3.85% higher than the original algorithm. It is verified that the improved YOLOv5 algorithm is very effective when applied to garbage classification, and it has social promotion significance and value. |
| first_indexed | 2025-11-15T09:37:43Z |
| format | Conference or Workshop Item |
| id | upm-37565 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T09:37:43Z |
| publishDate | 2023 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-375652023-09-28T03:37:46Z http://psasir.upm.edu.my/id/eprint/37565/ Domestic garbage target detection based on improved YOLOv5 algorithm Ma, Haohao Wu, Xuping As'arry, Azizan Han, Weiliang Mu, Tong Feng, Yanwei The output of household garbage has increased rapidly in the world, due to the development of the economy, the improvement of the living standards of residents, and the acceleration of urbanization. The process of manual garbage classification is time-consuming and laborious, and the effect is still not satisfactory. In order to reduce the intensity of manual garbage classification and improve the efficiency and accuracy of garbage classification, a new type of household garbage classification based on improved YOLOv5 algorithm visual recognition is designed. Make a data set for garbage detection, and after training on the improved YOLOv5 network framework, detect the status of garbage in real time. Experiments have proved that the accuracy of intelligent classification reached 98.27%, which is 3.85% higher than the original algorithm. It is verified that the improved YOLOv5 algorithm is very effective when applied to garbage classification, and it has social promotion significance and value. IEEE 2023 Conference or Workshop Item PeerReviewed Ma, Haohao and Wu, Xuping and As'arry, Azizan and Han, Weiliang and Mu, Tong and Feng, Yanwei (2023) Domestic garbage target detection based on improved YOLOv5 algorithm. In: 13th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE2023), 20-21 May 2023, Penang, Malaysia. (pp. 238-243). https://ieeexplore.ieee.org/document/10165597 10.1109/ISCAIE57739.2023.10165597 |
| spellingShingle | Ma, Haohao Wu, Xuping As'arry, Azizan Han, Weiliang Mu, Tong Feng, Yanwei Domestic garbage target detection based on improved YOLOv5 algorithm |
| title | Domestic garbage target detection based on improved YOLOv5 algorithm |
| title_full | Domestic garbage target detection based on improved YOLOv5 algorithm |
| title_fullStr | Domestic garbage target detection based on improved YOLOv5 algorithm |
| title_full_unstemmed | Domestic garbage target detection based on improved YOLOv5 algorithm |
| title_short | Domestic garbage target detection based on improved YOLOv5 algorithm |
| title_sort | domestic garbage target detection based on improved yolov5 algorithm |
| url | http://psasir.upm.edu.my/id/eprint/37565/ http://psasir.upm.edu.my/id/eprint/37565/ http://psasir.upm.edu.my/id/eprint/37565/ |