Abstract
In this paper, the model and its curriculum learning method of garbage hierarchical classification and corresponding operation mode decision in home environment are proposed from the perspective of cleaning robot. In order to realize the hierarchical learning of garbage attribute concept, this paper designs a learning model with iterative feedback network as the backbone network. In the early stage of iteration, the model focuses on learning the state of garbage, in the middle stage, it focuses on the appearance attributes of garbage, and the specific categories of garbage in the later stage. At the same time, the attention module is introduced to achieve different levels of feature expression learning, which further improves the performance of the model. The evaluation was conducted on the collected garbage data set and the public CIFAR-100 and Stanford Cars data sets, which verified the effectiveness and wide applicability of the proposed method.
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Funding
This work was supported by Key Project of Guangdong Fundamental and Application Fundamental Research Joint Fund [2020B1515120050] and the Joint Fund for Regional Innovation and Development of NSFC under [Grant U19A2083]; and the Science and Technology Research and Major Achievements Transformation Project of Strategic Emerging Industries in Hunan Province under [Grant 2019GK4007]; And it was supported by Natural Science Foundation of Hunan Province under [Grant 2020JJ4090 and 2020JJ4588].
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dongbo Zhang, Panbo Fu, Feng Yin, and Hongzhong Tang. The first draft of the manuscript was written by Panbo Fu. All authors read and approved the manuscript.
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Fu, P., Zhang, D., Yin, F. et al. The multi-mode operation decision of cleaning robot based on curriculum learning strategy and feedback network. Neural Comput & Applic 34, 9955–9966 (2022). https://doi.org/10.1007/s00521-022-06980-5
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DOI: https://doi.org/10.1007/s00521-022-06980-5