Image processing method, electronic device and computer readable storage medium

Abstract

The embodiment of the invention discloses an image processing method, an electronic device and a computer readable storage medium, and relates to the technical field of data processing. The image processing method comprises the following steps: analyzing each traffic element in each frame of image one by one, judging whether the traffic elements are repeated in a plurality of frames of images which are adjacent in time or not, and marking the repeated traffic elements; and deleting redundant images in all the images, wherein all the traffic elements in the redundant images are marked as the repeated traffic elements. The method, the electronic device and the computer readable storage medium can be used for improving the universality of redundant image screening and the compression rate ofrepeated traffic elements in the image.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to an image processing method, an electronic device, and a computer-readable storage medium. Background technique [0002] Map crowdsourcing is an emerging data production mode in the data collection industry. It subcontracts map data collection work to many users. Subcontracting users only need to install a simple application on their mobile phones to participate in crowdsourcing data collection. It has the advantages of low acquisition cost, fast return speed, and wide coverage. At present, in the face of billions of image data collected by many subcontracted users every year, how to use image intelligent recognition technology to promote the realization of production automation and reduce labor costs is a very urgent task. Among them, how to remove redundant images in many image data is the most important task in this task. [0003] Generally speaking, clusterin...

AI Technical Summary

Problems solved by technology

[0004] However, in real life, traffic lights have physical separation characteristics, but other traffic elements such as street signs can hardly find such physical separation characteristics

Therefore, the existing schemes are only suitable for the aggregation of certain types of traffic elements, and are only suitable for specific scenarios, and the universality is not strong

Moreover, the existing scheme is to aggregate the whole image. If there are repeated physical points and non-repeated physical points in two consecutive images, since the physical distance between the two images is relatively close, non-repeated, useful The physical points are removed

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