Target detection method based on multi-source heterogeneous data cognitive fusion

Abstract

The invention provides a target detection method based on multi-source heterogeneous data cognitive fusion. The method is used for solving the technical problem of low target detection precision caused by a shielded target contained in an image in the prior art, and comprises the following steps of: preprocessing data; obtaining a training data set and a test data set; constructing a target detection model H based on multi-source heterogeneous data cognitive fusion; performing iterative training on the target detection model H based on multi-source heterogeneous data cognitive fusion; obtaining target detection results. On the basis of a target detection network, an input optical remote sensing image and an SAR image are fused, an optical remote sensing feature map and an SAR feature map after feature extraction are fused, and the detection results of the optical remote sensing image and the SAR image are fused, so that a target detection model learns the features of the SAR image andthe features of the optical remote sensing image, and the technical problem that the detection precision of the image containing a shielded target is low is solved.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to a target detection method, and further relates to an automatic target detection method based on cognitive fusion of multi-source heterogeneous data. Background technique [0002] Object detection refers to detecting the category and frame coordinates of objects of interest in a given image or video. Object detection exists in various industries with its wide range of application requirements, such as biomedical, road monitoring, aerospace, Industrial manufacturing, cultural display, etc. [0003] Object detection algorithms are divided into two categories: traditional object detection algorithms and learning-based object detection algorithms. The traditional target detection algorithm mainly consists of three parts: image preprocessing, feature extraction, and target classification. Mainly through a series of images that have been processed and processed, feature extraction ...

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to address the above-mentioned deficiencies in the prior art, and propose a target detection method based on cognitive fusion of multi-source heterogeneous data to solve In the prior art, there is a technical problem that the target detection accuracy is low due to the occluded target in the image

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