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 F1 value of 95.44%, which showed that the method proposed in this paper has a good effect on the fine-grained classification of butterflies.


Civil, Architectural and Environmental Engineering


This work was supported by the Changsha Municipal Natural Science Foundation (Grant No. kq2014160), in part by the National Natural Science Foundation in China (Grant No. 61703441), in part by the key projects of the Department of Education Hunan Province (Grant No. 19A511), and in part by the Hunan Key Laboratory of Intelligent Logistics Technology (2019TP1015).

Keywords and Phrases

Butterfly Classification; MRDA-MGFSNet; Multi-Granularity Feature Sharer; Multi-Rate Dilated Attention Mechanism

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Article - Journal

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Publication Date

01 Aug 2021