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A semantic segmentation algorithm for fashion images based on modified mask RCNN

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Abstract

The semantic segmentation of human body images has huge application potential in many fields, such as autonomous driving, artificial intelligence (AI) face changing, and virtual try-on. Nowadays, many researchers use additional human body posture information to generate multi-level human body analysis images. However, the existing method has limitations when faced with multiple poses and overlapping targets. In this paper, a novel algorithm based on Mask RCNN which has pixel-level accuracy is proposed. In the feature extraction process, a multi-scale feature fusion module applying dilated convolution is proposed to obtain richer semantic information from different perceptual fields. We added a small residual module to the original residual unit structure to increase the size of the receptive field of each layer to capture details and global characteristics. Three convolution kernels with different ratios are designed to obtain receptive fields of different scales. The experimental results show that our method has better performance while considering both object positioning and target classification.

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Funding

This work was supported by National Natural Science Foundation of China (No. 61976105) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX22_2342).

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Authors and Affiliations

  1. Key Laboratory of Eco-textiles, Ministry of Education, Jiangnan University, No. 1800, Lihu Avenue, Wuxi, 214122, Jiangsu, China

    Wentao He, Jing’an Wang, Lei Wang, Ruru Pan & Weidong Gao

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  1. Wentao He
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  2. Jing’an Wang
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  3. Lei Wang
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  4. Ruru Pan
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  5. Weidong Gao
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Correspondence to Ruru Pan.

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Wentao He, Jing’an Wang, Lei Wang, Ruru Pan* and Weidong Gao declare that they have no conflict of interest.

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He, W., Wang, J., Wang, L. et al. A semantic segmentation algorithm for fashion images based on modified mask RCNN. Multimed Tools Appl 82, 28427–28444 (2023). https://doi.org/10.1007/s11042-023-14958-1

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  • DOI: https://doi.org/10.1007/s11042-023-14958-1

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