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YOLO-MTG: a lightweight YOLO model for multi-target garbage detection

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Abstract

With wide adoption of deep learning technology in AI, intelligent garbage detection has become a hot research topic. However, existing datasets currently used for garbage detection rarely involves multi-category and multi-target garbage that are densely accumulated in actual garbage detection scenarios. In addition, many existing garbage detection models have such problems as low detection efficiency and difficulties in integration with resource-constrained devices. To address the above situations, this study proposes a lightweight YOLO model for multi-target garbage detection (YOLO-MTG). This model is designed as follows: firstly, MobileViTv3, a lightweight hybrid network, serves as the feature extraction network to encode global representations, enhancing the model's ability of discriminating dense targets. Secondly, MobileViT block, the feature extraction unit, is optimized with combination of EfficientFormer and dynamic convolution, aiming to enhance the model's feature extraction capability, focusing on essential feature information and reduce the redundancy in useless information. Finally, feature reuse techniques are deployed to reconstruct Neck to minimize the loss of channel information in the feature transmission process, and maintain the strong feature fusion ability of the model. The experimental results on the self-built multi-target garbage (MTG) dataset show that YOLO-MTG achieves 95.4% mean average precision (mAP) with only 3.4 M parameters, and it is superior to other state-of-the-art (SOTA) methods. This work contributes new insights into the field of garbage detection, aiming to advance garbage classification for practical engineering applications.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive comments and suggestions, which significantly contributed to improving the manuscript. This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY24F020005.

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, 311300, China

    Zhongyi **a, Houkui Zhou, Guangqun Zhang, Junguo Hu & Tao He

  2. Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Hangzhou, 311300, China

    Zhongyi **a, Houkui Zhou, Guangqun Zhang, Junguo Hu & Tao He

  3. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China

    Huimin Yu & Haoji Hu

  4. State Key Laboratory of CAD & CG, Hangzhou, 310027, China

    Huimin Yu

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  1. Zhongyi **a
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Contributions

Zhongyi **a: Conceptualization, Methodology, Software, Formal analysis, Visualization, Writing – original draft. Houkui Zhou: Project administration, Data curation, Writing – review & editing. Huimin Yu: Data curation, Supervision, Haoji Hu: Investigation, Supervision. Guangqun Zhang: Investigation, Funding acquisition. Junguo Hu: Project administration, Funding acquisition. Tao He: Investigation.

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Correspondence to Houkui Zhou.

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**a, Z., Zhou, H., Yu, H. et al. YOLO-MTG: a lightweight YOLO model for multi-target garbage detection. SIViP (2024). https://doi.org/10.1007/s11760-024-03220-2

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