MRDA-MGFSNet: Network Based on a Multi-Rate Dilated Attention Mechanism and Multi-Granularity Feature Sharer for Image-Based Butterflies Fine-Grained Classification

文献类型： 外文期刊

第一作者： Li, Maopeng

作者： Li, Maopeng;Zhou, Guoxiong;Cai, Weiwei;Li, Jiayong;Li, Mingxuan;He, Mingfang;Hu, Yahui;Li, Liujun

作者机构：

关键词： butterfly classification; MRDA-MGFSNet; multi-rate dilated attention mechanism; multi-granularity feature sharer

期刊名称：SYMMETRY-BASEL （ 影响因子：2.713； 五年影响因子：2.612 ）

ISSN：

年卷期： 2021 年 13 卷 8 期

页码：

收录情况： SCI

摘要： Aiming at solving the problems of high background complexity of some butterfly images and the difficulty in identifying them caused by their small inter-class variance, we propose a new fine-grained butterfly classification architecture, called Network based on Multi-rate Dilated Attention Mechanism and Multi-granularity Feature Sharer (MRDA-MGFSNet). First, in this network, in order to effectively identify similar patterns between butterflies and suppress the information that is similar to the butterfly's features in the background but is invalid, a Multi-rate Dilated Attention Mechanism (MRDA) with a symmetrical structure which assigns different weights to channel and spatial features is designed. Second, fusing the multi-scale receptive field module with the depthwise separable convolution module, a Multi-granularity Feature Sharer (MGFS), which can better solve the recognition problem of a small inter-class variance and reduce the increase in parameters caused by multi-scale receptive fields, is proposed. In order to verify the feasibility and effectiveness of the model in a complex environment, compared with the existing methods, our proposed method obtained a mAP of 96.64%, and an F-1 value of 95.44%, which showed that the method proposed in this paper has a good effect on the fine-grained classification of butterflies.

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