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Brain-inspired filtering Network for small infrared target detection

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Abstract

Small infrared target detection is a challenge in computer vision because small infrared target occupies fewer pixels and the environment around the small infrared targets is complex. Although numerous small infrared target detection methods have been proposed, training an end-to-end deep detection model for small infrared target has not been thoroughly investigated. In this paper, we design a brain-inspired neural network (FilterDet) for small infrared target detection. There are two inter-connected modules in FilterDet, namely the brain-inspired filtering module and the target detection module. Brain-inspired filtering module consists of bottom-up filtering module and top-down filtering module which are modeled by bottom-up filtering mechanism and top-down filtering mechanism of the human brain, aiming to filter out the complex environment and interference. Target detection module takes the filtered infrared images as input and performs small infrared target detection. To train FilterDet in an end-to-end way, the loss function is designed by multi-task loss. Furthermore, we build a synthetic single frame infrared image set by generating synthetic infrared images with small targets. Comparative experiments are conducted on three real infrared image sequences and the single frame infrared image set to demonstrate the detection performance of FilterDet. The results show FilterDet has better performance for small infrared target detection compared with other detectors.

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Acknowledgements

The authors acknowledge National Natural Science Foundation of China (Grant no. 62201114) and the Fundamental Research Funds for the Central Universities (Grant no. 3132022242).

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  1. College of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China

    Ju Moran & Hu Qing

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  1. Ju Moran
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  2. Hu Qing
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Correspondence to Ju Moran.

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Moran, J., Qing, H. Brain-inspired filtering Network for small infrared target detection. Multimed Tools Appl 82, 28405–28426 (2023). https://doi.org/10.1007/s11042-023-14762-x

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