Optical remote sensing image target detection method based on extended residual convolution

Abstract

The invention discloses a depth convolution network optical remote sensing image target detection method based on extended residual convolution, which solves the problems of low detection accuracy rate and high false alarm rate of optical remote sensing image plane and ship in the prior art. The implementation steps are as follows: constructing a test data set; construct training data set; constructing a target detection network based on extended residual convolution for extended feature receptive field,training target detection network based on extended residual convolution using training data set; using the trained target detection network based on extended residual convolution to detect the target from the test data set, outputting test results. The network constructed by the inventionis more suitable for target detection of an optical remote sensing image by using an expanded residual convolution module and feature fusion, and not only improves the accuracy of a common target, butalso obviously improves the accuracy of small target detection for the optical remote sensing image. The method is used for object detection in optical remote sensing images.

Description

Technical field [0001] The present invention belongs to the technical field of image processing, and further relates to an optical remote sensing image target detection technology, in particular to an optical remote sensing image target detection method based on dilated residual convolution. The invention can be applied to the ground object detection of aircraft and ships in different regions of the optical remote sensing image. Background technique [0002] Target detection technology is one of the core issues in the field of computer vision. Remote sensing target detection uses images captured by remote sensing satellites as data sources and uses image processing technology to locate and classify objects of interest in the images. Remote sensing target detection is an important part of remote sensing application technology. It can capture attack targets and provide accurate location information in high-tech military confrontation. It is of vital significance in the military fie...

AI Technical Summary

Problems solved by technology

This method can accurately and richly represent the features of the target, and can well extract the target candidate frame, and reduce the candidate frame extraction time by sharing the convolution parameters. However, this method still has the disadvantage that due to the size of the optical remote sensing image Large and low resolution, especially in the detection of ships, the ship target is small and the characteristics of the ship are often similar to the characteristics of some long buildings or large vehicle containers, which makes this method difficult to perform optical remote sensing image targets. Objects on land are often misdetected as ships during detection

However, the shortcomings of this method are: first, the detection process is divided into multiple parts in the target detection, and different networks are used for detection and segmentation, which leads to high complexity of the method

Second, due to multiple downsampling when the network extracts features, the receptive field of the network features is too large, which is not conducive to the regression task

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