REEM modulates the soft-IoU objective with ground-truth local SCR computed from the input to emphasize low-visibility targets during training of a U-Net for infrared small target detection, yielding higher IoU and Pd with lower FA at no inference cost.
arXiv preprint arXiv:2001.05852 (2019)
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A monotonic diff-based scale loss and learnable Gaussian convolution with adaptive pinwheel masking improve mIoU, Pd, and Fa for infrared small target detection on three benchmarks.
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SCR-Guided Difficulty-Aware Optimization for Infrared Small Target Detection
REEM modulates the soft-IoU objective with ground-truth local SCR computed from the input to emphasize low-visibility targets during training of a U-Net for infrared small target detection, yielding higher IoU and Pd with lower FA at no inference cost.
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Revisiting the Scale Loss Function and Gaussian-Shape Convolution for Infrared Small Target Detection
A monotonic diff-based scale loss and learnable Gaussian convolution with adaptive pinwheel masking improve mIoU, Pd, and Fa for infrared small target detection on three benchmarks.