Wang, Z., Shen, H., Jiang, W., & Huang, K. (2024). A Fourier-Transform-Based Framework With Asymptotic Attention for Mobile Thermal InfraRed Object Detection. IEEE Sensors Journal, 24(13), 21012–21024. https://doi.org/10.1109/jsen.2024.3399193
Abstract:
Thermal InfraRed (TIR) technology has emerged as a significant tool
in autonomous driving systems. Unlike natural images, TIR images are
distinguished by their enriched thermal and illumination information while
lacking chromatic contrast. Traditional object detection on natural images
normally use deep neural networks based on convolutional layers
or attention modules. However, TIR-based object detection necessitates
high computational efficiency to eliminate the extraction of redundant
chromatic features. Furthermore, the robust space-frequency perception
and expansive receptive field are critical due to the distinct brightness and
contour features of TIR images. In this paper, we propose a novel network,
namely Lightweight-Fourier-Transform Detector (LFTDet), meticulously
designed to strike a balance between computational efficiency and
accuracy in TIR object detection. Specifically, our innovative Fourier
Transform-Efficient Layer Aggregation Network (FT-ELAN) backbone
takes advantage of Fourier Transform (FT) in synergy with deep neural
networks. In addition, we propose the detection neck called Asymptotic
Attention-based Feature Pyramid Network (AA-FPN), which integrates
the SimA mechanism in the asymptotic structure to facilitate
the FT-based operation. Extensive experiments conducted on FLIR and
LLVIP datasets demonstrate that LFTDet surpasses all baselines while
maintaining an extremely low computational cost. Code is available at
https://github.com/zeyuwang-zju/LFTDet.
License type:
Publisher Copyright
Funding Info:
National Key Research and Development Program of China Grant 2020AAA0109002