Remote sensing image target detection method based on deep evolution pruning convolutional network

Abstract

The invention discloses a remote sensing image target detection method based on a deep evolution pruning convolutional network. The problem that the detection speed and the detection precision are notglobally and effectively optimized at the same time in existing remote sensing image target detection is solved. The method comprises the specific steps of processing a data set; constructing a deepconvolution feature extraction subnet; constructing a full convolution FCN detection subnet; constructing and training a deep convolution target detection network; constructing and training a target detection network based on a deep evolution pruning convolutional network; performing target detection on the test data set by using the trained model; and outputting test results. According to the method, a reverse residual structure is constructed by using depth separable convolution, so that the model parameter quantity is greatly reduced while the detection precision is high; the target detection network is combined with evolutionary pruning to realize global acceleration. The method greatly reduces the calculation amount, remarkably improves the target detection speed, is high in detectionprecision, and is used for the quick and accurate detection of small targets in a remote sensing image, such as an airplane and a ship.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to remote sensing image target detection, specifically a remote sensing image target detection method based on deep evolutionary pruning convolutional network, which can be applied to aircrafts and ships in different areas in remote sensing images ground object detection. Background technique [0002] Target detection technology is one of the core issues in the field of computer vision. Remote sensing image target detection refers to the use of images captured by remote sensing satellites as data sources, and the use of image processing technology to locate and classify objects of interest in the images. Remote sensing image target detection, as a key technology in the application of remote sensing images, can capture attack targets and provide accurate location and category information in high-tech military confrontation. It has a significant impact on the military f...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, due to the large size of the remote sensing image, low resolution, small target size and blurred target edge, the existing methods often fail to learn the characteristics of the target when performing remote sensing image target detection, which leads to the failure of target detection. The accuracy rate is low, and due to the huge amount of data in remote sensing images and the huge amount of parameters in the network model, the detection speed is greatly limited

[0004] The efficiency and accuracy of existing target detection technologies are often incompatible

The second-order detection model such as FasterR-CNN has high accuracy, but also brings a huge amount of calculation; the first-order detection model such as YOLO and SSD has a faster calculation speed, but the accuracy rate is not satisfactory

However, the shortcomings of this method are that there is a large amount of redundant information in the residual network ResNet and the feature pyramid network FPN, and the amount of parameters and calculations is large, which affects the computational complexity and speed of the model, and does not meet the requirements of embedding. Deployment requirements of type equipment

However, the disadvantage of this method is that the global discriminant function based on the prior knowledge of the filter needs to be designed according to the specific task. Using the same global discriminant function in different applications may introduce discriminant bias, resulting in poor overall accuracy. lost

[0007] At present, when the target detection algorithm is used for target detection in large-scale, low-resolution optical remote sensing images, it is limited by the huge amount of data and model parameters, and there are problems such as small target size and blurred target edges. Accuracy and detection speed cannot be optimized at the same time, and it is difficult to perform fast and accurate detection of optical remote sensing images

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