LAUSR

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.