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.
M. Li et al., "MRDA-MGFSNet: Network based on a Multi-Rate Dilated Attention Mechanism and Multi-Granularity Feature Sharer for Image-Based Butterflies Fine-Grained Classification," Symmetry, vol. 13, no. 8, article no. 1351, MDPI, Aug 2021.
The definitive version is available at https://doi.org/10.3390/sym13081351
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Butterfly Classification; MRDA-MGFSNet; Multi-Granularity Feature Sharer; Multi-Rate Dilated Attention Mechanism
International Standard Serial Number (ISSN)
Article - Journal
© 2021 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
01 Aug 2021