Filtering method of prediction frame, electronic equipment and storage medium

Abstract

The embodiment of the invention relates to the field of image processing, and discloses a filtering method of a prediction frame, electronic equipment and a storage medium. In a part of embodiments ofthe present application, the prediction frame filtering method comprises: determining an effective prediction frame and a prediction frame to be filtered in a distorted image; for each to-be-filteredprediction frame, determining whether to reserve the to-be-filtered prediction frame or not according to the overlapping degree of the to-be-filtered prediction frame and the effective prediction frame and the overlapping degree threshold value corresponding to the to-be-filtered prediction frame, and determining the overlapping degree threshold value according to the reference position information corresponding to the to-be-filtered prediction frame. In the implementation, in the process of filtering the prediction frames, the overlapping degree threshold values are set for different prediction frames respectively, and compared with a mode of using a fixed overlapping degree threshold value, the situation that the prediction frames in the distorted image are mistakenly deleted or omittedis reduced.

Description

technical field [0001] The embodiments of the present invention relate to the field of image processing, and in particular to a method for filtering prediction frames, electronic equipment and storage media. Background technique [0002] A fisheye lens is a special lens with a wider angle of view and a shorter focal length than standard lenses. Due to the large viewing angle and short focal length of the fisheye lens, the captured fisheye images are distorted. At present, the industry mainly uses fisheye lenses as visual sensors to collect fisheye images. [0003] However, the inventors have found that at least the following problems exist in the prior art: at present, the accuracy rate of the deep learning target detection algorithm is already significantly higher than that of the traditional target detection and recognition algorithm. The accuracy of deep learning algorithms is greatly affected by the training data. When performing a deep learning target detection algor...

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Problems solved by technology

[0003] However, the inventors found that there are at least the following problems in the prior art: at present, the accuracy rate of the deep learning target detection algorithm is already significantly higher than that of the traditional target detection and recognition algorithm

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