Graphic Perception System for Visually Impaired Groups

نویسندگان

چکیده

In the age of internet, demand visually impaired groups to perceive graphic images through tactile sense is becoming stronger and stronger. Image object recognition a basic task in field computer vision. recent years, deep neural networks have promoted development image recognition. However, existing methods generally problems details’ loss edge refinement, which cannot improve accuracy rate for groups. order solve this problem, study proposes perception system, improves attention mechanism. This system mainly consists three modules: mixing module (MAM), enhanced receptive (ERFM), multilevel fusion (MLAM). MAM can generate better semantic features, be used guide feature decoding process, so that aggregated features locate significant objects. ERFM enrich context information low-level input into MLAM. MLAM uses generated by current decoded features’ output ERFM, gradually recover boundary details cascading manner. Finally, proposed algorithm compared with other algorithms on PASCAL VOC MS-COCO data. Experimental results show method effectively

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ژورنال

عنوان ژورنال: Advances in multimedia

سال: 2022

ISSN: ['1687-5680', '1687-5699']

DOI: https://doi.org/10.1155/2022/8437979