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...
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| 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/ https://ieeexplore.ieee.org/document/10165597 |
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| Institution: | Universiti Putra Malaysia |
| Summary: | 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. |
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