Zai-Ping LIN, Bo-Yang LI, Miao LI, Long-Guang WANG, Tian-Hao WU, Yi-Hang LUO, Chao XIAO, Ruo-Jing LI, Wei An. Light-weight infrared small target detection combining cross-scale feature fusion with bottleneck attention module[J]. Journal of Infrared and Millimeter Waves, 2022, 41(6): 1102
- Journal of Infrared and Millimeter Waves
- Vol. 41, Issue 6, 1102 (2022)
Fig. 1. The main challenges of infrared small target detection.
Fig. 2. An illustration of the proposed light-weighted infrared small target detection network
Fig. 3. The network of classic U-shape and our proposed LIRDNet
Fig. 4. Bottleneck attention module
Fig. 5. Samples of eight connected neighborhood clustering module. If the eight neighborhoods of two candidate points have intersection area，they are identified as the same target ID.
Fig. 6. Examples of（a）original images and corresponding qualitative comparison results on（b）Tophat，（c）IPI，（d）RIPT，（e）ACM，（f）DNANet，（g）LIRDNet，（h）ground truth masks.
Fig. 7. Examples of（a）original images and corresponding 3D visualization results on（b）Tophat，（c）IPI，（d）RIPT，（e）ACM，（f）DNANet，（g）LIRDNet，（h）ground truth masks.
Fig. 8. The ROC curve of our proposed LIRDNet under different signal-clutter-ratio（SCR）values（a）SCR<3，（b）3
Fig. 9. Visualization map of our proposed LIRDNet and backbone network ResUnet on different convolutional layers
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Table 1. Number of model parameters for LIRDNet and the corresponding size of input and output feature map
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Table 2. Performance of different methods on IoU, Pd, and Fa
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Table 3. Performance of different deep learning-based methods on the number of model parameters, FLOPs, IoU, Pd, and Fa
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Table 4. Inference time and FLOPs performance of different deep learning-based methods on different computational units (Smart Phone-Chip, PC-GPU)
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Table 5. Ablation study on our proposed CFM module
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Table 6. Ablation study on our introduced BAM module
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