HRANet: histogram-residual-attention network used to measure neatness of toy placement

Ying Zang, Chaotao Ding, Wenjun Hu, Chenglong Fu

Abstract: Whether the products on the shelf are neatly displayed is one of the important factors affecting consumers’ desire to buy, which is more obvious in the toy industry. In order to detect the neatness of the toy commodities on the shelf, we propose a method based on texture recognition to measure the neatness of the commodities. Since the whole of neat toy commodities will show regular texture features, in the field of computer vision, how to extract effective texture features from image parameters has always been the focus and difficulty of texture analysis. In this paper, we use a multiquadratic kernel modeling learnable local histogram layer to extract effective texture features and use the convolutional block attention module to filter out features that are more conducive to classification, proposed a new network named histogram residual attention network (HRANet). In the test performance stage, we use the proposed HRANet to test on DTD, MINC-2500, GTOS-mobile, and KTH-T2b datasets, and the accuracy is significantly higher than the original HistRes. Finally, we use the features obtained by HRANet to represent the features of product display and use the cosine distance to measure the similarity of product regions, which effectively quantifies the neatness of product display.