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Learning feature fusion for target detection based on polarimetric imaging

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Abstract

We propose a polarimetric imaging processing method based on feature fusion and apply it to the task of target detection. Four images with distinct polarization orientations were used as one parallel input, and they were fused into a single feature map with richer feature information. We designed a learning feature fusion method using convolutional neural networks (CNNs). The fusion strategy was derived from training. Meanwhile, we generated a dataset involving one original image, four polarization orientation images, ground truth masks, and bounding boxes. The effectiveness of our method was compared to that of conventional deep learning methods. Experimental results revealed that our method gets a 0.80 mean average precision (mAP) and a 0.09 miss rate (MR), which are both better than the conventional deep learning method.

© 2021 Optical Society of America

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Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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