Multi-scale local intensity and gradient networks for infrared small target detection

Zehao Zhao; Yukun Ma; ZhiQiang Chen; Wang Hao; Yaohong Chen

doi:10.1117/12.2691798

6 November 2023 Multi-scale local intensity and gradient networks for infrared small target detection

Zehao Zhao, Yukun Ma, ZhiQiang Chen, Wang Hao, Yaohong Chen

Proceedings Volume 12921, Third International Computing Imaging Conference (CITA 2023); 129214E (2023) https://doi.org/10.1117/12.2691798
Event: Third International Computing Imaging Conference (CITA 2023), 2023, Sydney, Australia

Abstract

Robust small infrared target detection has becoming increasingly important in security and defense applications. Current detection methods can be divided into two parts. The performance of the model-driven methods is limited by the prior assumption parameters, while data-driven methods perform poor in semantic feature extraction due to the lack of the shape characteristic. In this paper, we proposed to embed a model-driven module into a convolution network, which has clear advantages on detection performance and running time. Our method consists of a backbone based on dilated convolution, a multi-scale local intensity and gradient module, and a segmentation head. Experimental results using extended SIRST dataset demonstrated that the proposed method achieves a good balance between the detection performance and running time.

Citation Download Citation

Zehao Zhao, Yukun Ma, ZhiQiang Chen, Wang Hao, and Yaohong Chen "Multi-scale local intensity and gradient networks for infrared small target detection", Proc. SPIE 12921, Third International Computing Imaging Conference (CITA 2023), 129214E (6 November 2023); https://doi.org/10.1117/12.2691798

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KEYWORDS

Target detection

Small targets

Infrared imaging

Convolution

Semantics

Performance modeling

Feature fusion

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