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

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.