Fine-grained image classification method based on feature pyramid and global average pooling

Abstract

The invention discloses a fine-grained image classification method based on a feature pyramid and global average pooling. The fine-grained image classification method comprises the following steps: step 1, inputting an image into a convolutional layer of a pre-trained convolutional neural network to obtain a multi-channel feature map; 2, enabling the multichannel feature map to pass through a global average pooling layer to obtain a saliency map of an input image, and extracting the position information of a target; step 3, enabling the feature pyramid network to extract the features of the multi-channel feature map and perform prediction to obtain K local areas with the maximum information amount; and step 4, aggregating the local features of the K local regions and global features obtained by the input image through the convolutional neural network to predict and output a final identification category. According to the method, the influence of background noise is reduced, the local region selection robustness is enhanced, and the recognition precision is improved.

Description

technical field [0001] The invention relates to a fine-grained image classification method, in particular to a fine-grained image classification method based on feature pyramid and global average pooling. Background technique [0002] Fine-grained image recognition is a concept in the field of image processing. Traditional image recognition can only recognize the large categories of objects in the image, which is called coarse-grained image recognition. However, there are usually many subcategories under the same category, and traditional image recognition methods cannot determine the specific subcategory to which the target belongs. Fine-grained image recognition can classify the targets in the image more finely, and the granularity of the classification is finer. It is required to determine the specific subcategory of the target under the large category to meet the requirements of higher image recognition in different scenarios. Require. [0003] Early fine-grained class...

AI Technical Summary

Problems solved by technology

[0008] In view of the above-mentioned defects in the prior art, the task of the present invention is to provide a fine-grained image classification method based on feature pyramid and global average pooling, which uses less computational overhead to solve the noise problem in the target positioning area, and improves the efficiency of target object extraction. feature robustness

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