基于SASK和双分支结构的服装图像识别方法

doi:10.3969/j.issn.0255-8297.2023.06.005

Abstract

Abstract: Due to the limited performance of the existing recognition methods for clothing images with varying brightness and scales, we propose a neural network model based on spatial attention selective kernel（SASK） and double branch structure in this paper.Firstly, a double branch neural network that incorporates jump connection, dense connection, multi-scale and channel splitting is established to fully extract the overall features of clothing images. Secondly, the SASK module based on the spatial attention mechanism is designed to enable the network to focus on the morphological feature information of clothing images for accurate recognition. Experimental results show that our method not only improves the recognition accuracy of typical clothing datasets compared to existing mainstream methods, but also performs effectively on image datasets with different brightness and scales.

Key words: clothing image recognition, spatial attention selective kernel(SASK), twobranch neural network, different brightness, different scales

CLC Number:

TP391.4

ZHOU Xiaohui, YU Lei, ZHANG Ruiting, XIONG Bangshu, OU Qiaofeng. Clothing Image Recognition Method Based on SASK and Double Branch Structure[J]. Journal of Applied Sciences, 2023, 41(6): 967-977.

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Clothing Image Recognition Method Based on SASK and Double Branch Structure

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