Rock image retrieval method and system

Abstract

The invention discloses a rock image retrieval method which comprises: acquiring image data in real time, and inputting the image data into a trained deep learning network model to obtain a retrievalresult graph. The training process of the deep learning network model comprises: constructing a rock image data set by utilizing collected image data; inputting the data set into a network, and enabling the network to actively convert feature mapping in space after the data set is processed by a space transfer module; and inputting the processed data into a multi-granularity network, calculating atotal loss function and an mAP value of the model, and after multiple calculations, when the loss function tends to be stable and the mAP value reaches a peak value, completing training of the deep learning network model. The situation that only representation is used for classifying the rock images is avoided, meanwhile, the fine-grained characteristics of the rock images are extracted more accurately, and the retrieval accuracy of the rock images can be improved under the conditions that sundries are shielded, the number of samples is small, the quality is low, and information is lost.

Description

technical field [0001] The invention relates to the field of image retrieval in computer vision technology, in particular to a rock image retrieval method and system thereof. Background technique [0002] China has a land area of ​​9,634,057 square kilometers, rich and colorful geological phenomena, and 214 national geological parks. However, due to the relative specialization of geology, most people lack basic common sense, so that ordinary people cannot have a concrete understanding of geology even if they are in it. Therefore, the recognition and retrieval of rock images is of great significance. With the advent of the information age and the explosive growth of data in the era of big data, the retrieval and management of rock sample images in geological work has become particularly important, which not only facilitates the effective retrieval, organization and management of rock images in massive data for professionals, It can also be convenient for non-professionals, ...

AI Technical Summary

Problems solved by technology

[0004] However, this patent only uses the surface features of rocks for classification, such as outlines, colors and other coarse-grained features, which cannot describe the deep-level semantic information of rock images well; the difficulty of labeling rock images and the small amount of data lead to the lack of robustness of the trained model. Stickiness; ignores the influence of local features on the overall lithology

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