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Infrared dim and small target detection based on two-stage U-skip context aggregation network with a missed-detection-and-false-alarm combination loss

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Abstract

Infrared small target detection (ISTD) is a critical technique in both civil and military applications such as leak and defect inspection, cell segmentation for medicine analysis, early-warning systems and so on. Over the last decade, numerous ISTD methods have been proposed, such as methods based on image denoising, visual saliency detection, low-rank matrix recovery and traditional machine learning, but training an end-to-end deep model to detect small targets has not been fully investigated. In this regard, the paper proposes a novel deep model called UCAN for ISTD which concatenates two context aggregation networks and connects them using U-skip connections. A Missed-detection-and-False-alarm Combination(MFC) loss function, which is based on the Neyman-Pearson decision theory, is proposed to train the model and can well balance the detection rate and the false alarm rate. In addition, a two-stage detection scheme which involves a cascade of two UCANs is proposed to further improve the overall detection performance of ISTD. Extensive experiments on real infrared sequences and a single-frame image set and the comparison with state-of-the-art methods demonstrate the superiority of the proposed model.

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Acknowledgments

The paper is supported by National Natural Science Foundation of China (61703209,61773215).

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Authors and Affiliations

  1. Nanjing University of Science and Technology, Nanjing, China

    Huan Wang & Manshu Shi

  2. School of Automation, Southeast University, Nanjing, China

    Hong Li

Authors
  1. Huan Wang
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  2. Manshu Shi
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  3. Hong Li
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Correspondence to Huan Wang.

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Wang, H., Shi, M. & Li, H. Infrared dim and small target detection based on two-stage U-skip context aggregation network with a missed-detection-and-false-alarm combination loss. Multimed Tools Appl 79, 35383–35404 (2020). https://doi.org/10.1007/s11042-019-7643-z

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