Automatic detection method for multi-scale polymorphic target in two-dimensional image

Abstract

The invention provides an automatic detection method for a multi-scale polymorphic target in a two-dimensional image. The automatic detection method comprises the following steps: preprocessing the two-dimensional image; performing target labeling on the preprocessed image to complete the production of a data set; adding a spatial mapping layer into a basic feature extraction network of the targetdetection network, and fusing the feature map after spatial mapping and other feature maps together to form a feature pyramid to adapt to the change of a target in the two-dimensional image; completing region recommendation on a feature pyramid formed by the plurality of fused feature maps by using an anchor box with good prior; training the improved target detection network by using the manufactured data set, and carrying out cross validation for multiple times; and using the trained target detection model to detect the picture possibly containing the target, selecting a threshold to screenout a detection box with relatively high possibility of containing the target, carrying out non-maximum suppression on the screened detection box, and removing an overlapping box to obtain a final target detection result with relatively high accuracy.

Description

technical field [0001] The invention belongs to the field of image analysis and target detection, and in particular relates to an automatic detection method for multi-scale and multi-morphological targets in two-dimensional images. Background technique [0002] Targets to be recognized with geometric deformation are widely distributed in visual scenes, and the automatic detection of multi-scale and multi-morphological targets in two-dimensional images is conducive to fast and accurate target positioning and recognition in uncontrolled natural scenes. The current two-dimensional image target detection method is not robust to the target detection results with variable morphological scales, and relying on human observation and correction is time-consuming and laborious. Human subjective factors such as fatigue and experience will affect the accuracy and consistency of observation results. Contents of the invention [0003] Purpose of the invention: The technical problem to be...

AI Technical Summary

Problems solved by technology

[0003] Purpose of the invention: The technical problem to be solved by the present invention is to provide a method based on the convolutional neural network and basic image processing methods in deep learning for the poor detection effect of multi-scale and multi-morphological targets in existing two-dimensional images. The automatic detection method of multi-scale and multi-morphological targets in two-dimensional images has realized the accurate detection of multi-scale and multi-morphic targets

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