Data identification method and device, equipment and readable storage medium

Abstract

The invention discloses a data processing method and device, equipment and a readable storage medium, and the method comprises the steps: obtaining a target image, and identifying N prediction regions of a target object in the target image; obtaining a prediction object category and a prediction object label feature corresponding to the target object in each prediction area; obtaining the maximum class prediction probability from the class prediction probabilities corresponding to the N prediction object classes, and determining the prediction region corresponding to the prediction object class with the maximum class prediction probability as a target prediction region; determining a coverage area commonly covered by the residual prediction area and the target prediction area; determining the label feature similarity between the prediction object label features corresponding to the remaining prediction regions and the prediction object label features corresponding to the target prediction region; and determining an optimal prediction region of the target object in the target image according to the similarity between the coverage region and the label feature. According to the invention, the identification accuracy of the object in the image can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular to a data identification method, device, equipment and readable storage medium. Background technique [0002] Object detection is a very important computer vision task, its purpose is to identify the location of the object in the image and the category of the object. At present, the process of the entire target detection is mainly as follows: the convolutional neural network first outputs the detection frames of all possible objects in the image; then, based on the post-processing method of Non Maximum Suppression (NMS), these detection The optimal detection frame for each object is determined in the frame. [0003] Among them, for the post-processing method of non-maximum value suppression, the main processing flow is: first classify each detection frame according to the predicted object category of each detection frame, and classify the detection frames belonging ...

AI Technical Summary

Problems solved by technology

For example, if user a and user b are very close to each other in the image, their corresponding detection frames will also be very close, and the intersection and union ratio of the overlapping area is greater than the threshold, then the detection frame corresponding to user a or the detection frame corresponding to user b is likely to be blocked. Delete, that is, it is likely to delete the detection frame that should not b

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