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An automatic garbage detection using optimized YOLO model

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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.

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Data availability

The dataset analyzed in this study is available upon reasonable request.

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Acknowledgements

The research funding is provided by Universiti Malaya with project number IMG001-2022.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia

    Nur Athirah Zailan, Anis Salwa Mohd Khairuddin & Khairunnisa Hasikin

  2. School of Aerospace Engineering, Universiti Sains Malaysia, Engineering Campus, 14300, Nibong Tebal, Penang, Malaysia

    Mohamad Haniff Junos

  3. School Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Uswah Khairuddin

Authors
  1. Nur Athirah Zailan
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  2. Anis Salwa Mohd Khairuddin
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  3. Khairunnisa Hasikin
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  4. Mohamad Haniff Junos
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  5. Uswah Khairuddin
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Contributions

NAZ, MHJ and ASMK performed analysis, investigation, validation, and draft manuscript. KH and UK prepared conceptualization, methodology and figures. All authors reviewed the manuscript.

Corresponding author

Correspondence to Anis Salwa Mohd Khairuddin.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. All the authors listed have approved the manuscript that is enclosed.

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Zailan, N.A., Mohd Khairuddin, A.S., Hasikin, K. et al. An automatic garbage detection using optimized YOLO model. SIViP 18, 315–323 (2024). https://doi.org/10.1007/s11760-023-02736-3

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