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. 2023 Mar 10;9:e1262. doi: 10.7717/peerj-cs.1262

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© 2023 Liu et al.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.

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The CBS component is composed of the Convolutional layer+BatchNormalization+SiLU activation function. The Resunit component draws on the residual structure in the Resnet network and can play a role in building a deeper network. The CSP1_X and CSP2_X component draws on the CSPNet network structure. The FOCUS component is to slice the data, which can play a role in the down-sampling operation without information loss. The SPP component adopts the maximum pooling method of 1 × 1, 5 × 5, 9 × 9, and 13 × 13 for multi-scale fusion.