Multimodal polarization image simulated crater detection

Jun Wu,Xin Zhang,Xiao Liu,Feng Wang,Jingjing Zhang,Teng Li

doi:10.1117/1.jei.29.2.023027

Abstract

Most previous target detection methods are based on the physical properties of visible-light polarization images, depending on different targets and backgrounds. However, this process is not only complicated but also vulnerable to environmental noises. A multimodal fusion detection network based on the multimodal deep neural network architecture is proposed in this research. The multimodal fusion detection network integrates the high-level semantic information of visible-light polarization image in crater detection. The network contains the base network, the fusion network, and the detection network. Each of the base networks outputs a corresponding feature figure of polarization image, fused by the fusion network later to output a final fused feature figure, which is input into the detection network to detect the target in the image. To learn target characteristics effectively and improve the accuracy of target detection, we select the base network by comparing between VGG and ResNet networks and adopt the strategy of model parameter pretraining. The experimental results demonstrate that the simulated crater detection performance of the proposed method is superior to the traditional and single-modal-based methods in that the extracted polarization characteristics are beneficial to target detection.

Highlights

In the military field, cameras are usually employed to collect images after a heavy artillery test and the success of this experiment is determined according to the position of the crater in the image
Conclude the following: in multimodal fusion detection networks, polarization information is beneficial for target detection in visible-light polarization images
Our trained multimodal fusion detection model was tested on the images of the simulated crater dataset

Summary

Introduction

Cameras are usually employed to collect images after a heavy artillery test and the success of this experiment is determined according to the position of the crater in the image. Detection based on images with polarization[1] is a new approach in which a photoelectric imaging device is used to obtain the target scene radiation, spatial information, spectral information, and polarization information.[2] The evaluation requirements can be initially met using the difference in polarization characteristics between the target and the background to extract the target object. This process[3] is complicated, cumbersome, and usually inaccurate.

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Results

Conclusion