Image detection method and device

Abstract

The invention discloses an image detection method and device. The image detection method comprises the steps: obtaining a to-be-detected image, extracting a model according to a preset multi-scale feature, extracting multi-scale feature information of the to-be-detected image, carrying out dimension reduction on the multi-scale feature information; obtaining target image feature information; obtaining target image feature information according to the target image feature information, searching historical image feature information according to the target image feature information to obtain a search result, if the historical image feature information matched with the target image feature information exists in the search result, calling a historical detection record corresponding to the historical image feature information, and outputting the historical detection record as a detection result of the to-be-detected image. According to the image detection method, repeated detection is avoided, and richer features can be extracted, and the image can be better described.

Description

technical field [0001] The invention relates to the field of image detection, in particular to an image detection method and device. Background technique [0002] Feature extraction is a concept in computer vision and image processing. It refers to the use of computers to extract image information and determine whether each image point belongs to an image feature. The result is called a feature description or feature vector. Commonly used image features include color features, texture features, shape features, and spatial relationship features. Image feature extraction is widely used in various scenarios, such as face recognition or image review. [0003] In the prior art, only the features of the last convolutional layer are used to extract the fingerprint of the picture material, and only the features of the highest layer are used. The extracted features are not sufficient, and the features of the lower layers are not modeled. When it is necessary to judge the similari...

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, only the features of the last convolutional layer are used to extract the fingerprint of the image material, and only the features of the highest layer are used. The extracted features are not sufficient, and the features of the lower layers are not modeled.

When it is necessary to judge the similarity of the image, it is easy to misjudge the similarity of the image when the extracted features are not sufficient, resulting in repeated detection and increasing the amount of calculation of the system

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