针对红外小目标检测，提出自适应损失函数提升小目标识别能力。
Target Driven Adaptive Loss For Infrared Small Target Detection
- 基于局部区域的自适应损失机制，聚焦目标周围区域。
- 在三个数据集上均优于现有损失函数，尤其提升小尺度低对比度目标检测性能。
- 适合红外小目标检测任务，对弱信号目标更敏感。
我们提出一种目标驱动自适应（TDA）损失，用于提升红外小目标检测（IRSTD）性能。以往方法使用二值交叉熵损失或IoU损失训练分割模型，但存在两个问题：难以增强目标附近局部区域的检测性能，且对小尺度和低局部对比度目标鲁棒性不足。为此，TDA损失引入基于图像块的机制与自适应缩放及对比度调整策略，引导模型关注目标周围局部区域，并特别强化对小尺度和低对比度目标的关注。我们在三个红外小目标检测数据集上评估该方法，结果表明，TDA损失在各项指标上均优于现有损失函数。
原文摘要 · Abstract (English)
We propose a target driven adaptive (TDA) loss to enhance the performance of infrared small target detection (IRSTD). Prior works have used loss functions, such as binary cross-entropy loss and IoU loss, to train segmentation models for IRSTD. Minimizing these loss functions guides models to extract pixel-level features or global image context. However, they have two issues: improving detection performance for local regions around the targets and enhancing robustness to small scale and low local contrast. To address these issues, the proposed TDA loss introduces a patch-based mechanism, and an adaptive adjustment strategy to scale and local contrast. The proposed TDA loss leads the model to focus on local regions around the targets and pay particular attention to targets with smaller scales and lower local contrast. We evaluate the proposed method on three datasets for IRSTD. The results demonstrate that the proposed TDA loss achieves better detection performance than existing losses on these datasets.
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