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Freshness uniformity measurement network based on multi-layer feature fusion and histogram layer

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Abstract

The arrangement of products on supermarket freshness shelves exhibits a certain pattern and displays distinct texture characteristics. In recent years, many studies have applied texture extraction algorithms in deep learning, such as the Histogram Layer Residual Network (HistNet). However, this algorithm still has obvious disadvantages, such as neglecting the optimal representation of multi-scale texture features and lacking feature selection during extraction. To address these issues, this paper introduces a novel texture classification network—Multi-Scale Feature Histogram Network (MFHisNet). First, we design a Multi-Scale Feature Fusion Module (MF-Block) to achieve a multi-level representation of texture information. Then, we utilize an attention module (CBAM) to weight crucial information and suppress background interference for deeper level texture features. Experimental results demonstrate that the model achieves accuracies of 82.12 ±2.04\(\%\), 73.13±1.10\(\%\), and 83.46±0.62\(\%\) on the GTOS-mobile, DTD, and MINC-2500 datasets, respectively. Furthermore, based on the proposed model, we propose a measurement method that uses cosine similarity to measure the uniformity of freshness placement, and the effectiveness of this method was verified on the dataset we collected.

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Data availability

The GTOS-mobile dataset can be accessed via https://drive.google.com/file/d/1Hd1G7aKhsPPMbNrk4zHNJAzoXvUzWJ9M/view. The DTD dataset can be accessed via https://www.robots.ox.ac.uk/vgg/data/dtd/ and MINC-2500 dataset can be accessed via http://opensurfaces.cs.cornell.edu/static/minc/minc-2500.tar.gz.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61772252, the Scientific Research Foundation of the Education Department of Liaoning Province under Grant LJKZ0965, and the Huzhou Science and Technology Plan Project under Grants 2022GZ08 and 2023ZD2004.

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Authors and Affiliations

  1. School of Information Engineering, Huzhou University, Huzhou, 313000, China

    Ying Zang, Chunan Yu, Chenglong Fu, Qingshan Liu & Yong Zhang

  2. Huzhou Institute of Zhejiang University, Huzhou, 313000, China

    Zhenfeng Xue

  3. School of Computer and Information Technology, Liaoning Normal University, Dalian, 116081, China

    Yong Zhang

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  1. Ying Zang
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  2. Chunan Yu
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  3. Chenglong Fu
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  4. Zhenfeng Xue
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Contributions

YZ and YZconceived the idea. YZ and CY realized the idea and wrote the main manuscript text. CF and CY prepared all figures and tables. YZ, ZX, and QL provided supervision. All authors reviewed the manuscript.

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Correspondence to Yong Zhang.

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Zang, Y., Yu, C., Fu, C. et al. Freshness uniformity measurement network based on multi-layer feature fusion and histogram layer. SIViP 18, 1525–1538 (2024). https://doi.org/10.1007/s11760-023-02837-z

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