Zailan, Nur Athirah and Mohd Khairuddin, Anis Salwa and Hasikin, Khairunnisa and Junos, Mohamad Haniff and Khairuddin, Uswah (2024) An automatic garbage detection using optimized YOLO model. Signal Image and Video Processing, 18 (1). pp. 315-323. ISSN 18631703, DOI https://doi.org/10.1007/s11760-023-02736-3.
Full text not available from this repository.Abstract
Garbage pollution is an increasing global concern. Hence, the adoption of innovative solutions is important for controlling garbage pollution. In order to develop an efficient cleaner robot, it is very crucial to obtain visual information of floating garbage on the river. Deep learning has been actively applied over the past few years to tackle various problems. High-level, semantic, and advanced features can be learnt by deep learning models based on visual information. This is extremely important to detect and classify different types of floating garbage. This paper proposed an optimized You Only Look Once v4 Tiny model to detect floating garbage, mainly by improving the spatial pyramid pooling with average pooling, mish activation function, concatenated densely connected neural network, and hyperparameters optimization. The proposed model shows improved results of 74.89% mean average precision with a size of 16.4 MB, which can be concluded as the best trade-off among other models. The proposed model has promising results in terms of model size, detection time and memory space, which is feasible to be embedded in low-cost devices.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Malaya [Grant No: IMG001-2022] |
| Uncontrolled Keywords: | Computer vision; Debris; Deep learning; Image processing; Object detection |
| Subjects: | T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 26 Aug 2024 08:43 |
| Last Modified: | 26 Aug 2024 08:43 |
| URI: | http://eprints.um.edu.my/id/eprint/46014 |
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