Abstract

Traffic port stations are composed of buildings, infrastructure, and transportation vehicles. The target detection of traffic port stations in high-resolution remote sensing images needs to collect feature information of nearby small targets, comprehensively analyze and classify, and finally complete the traffic port station positioning. At present, deep learning methods based on convolutional neural networks have made great progress in single-target detection of high-resolution remote sensing images. How to show good adaptability to the recognition of multi-target complexes of high-resolution remote sensing images is a difficult point in the current remote sensing field. This paper constructs a novel high-resolution remote sensing image traffic port station detection model (Swin-HSTPS) to achieve high-resolution remote sensing image traffic port station detection (such as airports, ports) and improve the multi-target complex in high-resolution remote sensing images The recognition accuracy of high-resolution remote sensing images solves the problem of high-precision positioning by comprehensive analysis of the feature combination information of multiple small targets in high-resolution remote sensing images. The model combines the characteristics of the MixUp hybrid enhancement algorithm, and enhances the image feature information in the preprocessing stage. The PReLU activation function is added to the forward network of the Swin Transformer model network to construct a ResNet-like residual network and perform convolutional feature maps. Non-linear transformation strengthens the information interaction of each pixel block. This experiment evaluates the superiority of the model training by comparing the two indicators of average precision and average recall in the training phase. At the same time, in the prediction stage, the accuracy of the prediction target is measured by confidence. Experimental results show that the optimal average precision of the Swin-HSTPS reaches 85.3%, which is about 8% higher than the average precision of the Swin Transformer detection model. At the same time, the target prediction accuracy is also higher than the Swin Transformer detection model, which can accurately locate traffic port stations such as airports and ports in high-resolution remote sensing images. This model inherits the advantages of the Swin Transformer detection model, and is superior to mainstream models such as R-CNN and YOLOv5 in terms of the target prediction ability of high-resolution remote sensing image traffic port stations.

Highlights

  • High-resolution remote sensing images [1] are completely different from ordinary digital images in terms of acquisition methods

  • Based on the above problems, this paper summarizes the above problems as the problem of supervised information depth feature aggregation caused by the high-resolution remote sensing image traffic port station target detection representation method

  • High-resolution remote sensing image data are used in the training, testing, and prediction of target detection methods

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Summary

Introduction

High-resolution remote sensing images [1] are completely different from ordinary digital images in terms of acquisition methods. Remote sensing images are obtained by sensors on aerospace and aviation equipment, and most of the imaging uses the top imaging method, that is, overlooking the ground. This imaging method is far away from the ground, so the spatial resolution is low. Multi-source high-resolution remote sensing images include satellite images of different series, different orbits, and different inclination angles of meteorological satellites and high-resolution remote sensing satellites. Such images are different in resolution, target angle, target size, and rotation angle [3]

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