Passive non-line-of-sight imaging for hidden target detection

Hailu Wang; Minsun Chen; Hao Liu; Wenchang Lai; Guozhong Lei; Kai Han

doi:10.1117/12.3049350

26 September 2024 Passive non-line-of-sight imaging for hidden target detection

Hailu Wang, Minsun Chen, Hao Liu, Wenchang Lai, Guozhong Lei, Kai Han

Author Affiliations +

Proceedings Volume 13282, Second Advanced Imaging and Information Processing Conference (AIIP 2024); 132820R (2024) https://doi.org/10.1117/12.3049350
Event: Second Advanced Imaging and Information Processing Conference (AIIP 2024), 2024, Xining, China

Abstract

The rapidly developing non-line-of-sight (NLOS) imaging technology in recent years is capable of intelligent visual perception of concealed targets, holding broad application prospects in security, emergency rescue, autonomous driving, etc.. Compared to active methods, passive NLOS imaging is promising to in real-world scenarios due to its low cost. This paper uses the long-wave infrared (LWIR) to detect multiple hidden targets. In contrast to the visible band, LWIR exhibits a higher proportion of specular reflection scattering on common relay surface but cannot represent details such as texture. Furthermore, passive NLOS imaging reconstruction is an ill-posed problem, leading to sparse and blurred features, which poses significant challenges for multi-target detection tasks. To address this, the paper proposes a deep learning method for collaborative multi-task image reconstruction and detection. The detection loss is backpropagated and fused with the imaging enhancement loss to guide the NLOS target reconstruction process towards high-quality detection results. Comparative experiments are conducted in multi-person target scenarios between the latest target detection methods and our method. The results indicate that our proposed method exhibits the best performance in terms of detection accuracy, recall rate, and the F1-score. Additionally, this paper demonstrates the generalization of the proposed method at different distances ranging from 10 to 20 meters. The related results provide data and methodological support for the advancement of NLOS imaging towards practical applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hailu Wang, Minsun Chen, Hao Liu, Wenchang Lai, Guozhong Lei, and Kai Han "Passive non-line-of-sight imaging for hidden target detection", Proc. SPIE 13282, Second Advanced Imaging and Information Processing Conference (AIIP 2024), 132820R (26 September 2024); https://doi.org/10.1117/12.3049350

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KEYWORDS

Non line of sight propagation

Target detection

Image restoration

Object detection

Long wavelength infrared

Relays

Deep learning

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