Recently, deep learning-based single-frame infrared small target (SIRST) detection technology has made significant progress. However, existing infrared small target detection methods are often optimized for a fixed image resolution, a single wavelength, or a specific imaging system, limiting their breadth and flexibility in practical applications. Therefore, we propose a refined infrared small target detection scheme based on an adjustable sensitivity (AS) strategy and multi-scale fusion. Specifically, a multi-scale model fusion framework based on multi-scale direction-aware network (MSDA-Net) is constructed, which uses input images of multiple scales to train multiple models and fuses them. Multi-scale fusion helps characterize the shape, edge, and texture features of the target from different scales, making the model more accurate and reliable in locating the target. At the same time, we fully consider the characteristics of the infrared small target detection task and construct an edge enhancement difficulty mining (EEDM) loss. The EEDM loss helps alleviate the problem of category imbalance and guides the network to pay more attention to difficult target areas and edge features during training. In addition, we propose an adjustable sensitivity strategy for post-processing. This strategy significantly improves the detection rate of infrared small targets while ensuring segmentation accuracy. Extensive experimental results show that the proposed scheme achieves the best performance. Notably, this scheme won the first prize in the PRCV 2024 wide-area infrared small target detection competition.

翻译：近年来，基于深度学习的单帧红外弱小目标检测技术取得了显著进展。然而，现有的红外弱小目标检测方法通常针对固定的图像分辨率、单一波长或特定成像系统进行优化，限制了其在实际应用中的广度与灵活性。为此，我们提出了一种基于可调灵敏度策略与多尺度融合的精细化红外弱小目标检测方案。具体而言，构建了一个基于多尺度方向感知网络的多尺度模型融合框架，该框架利用多尺度输入图像训练多个模型并进行融合。多尺度融合有助于从不同尺度刻画目标的形状、边缘及纹理特征，使模型在定位目标时更为准确可靠。同时，我们充分考虑了红外弱小目标检测任务的特点，构建了边缘增强困难样本挖掘损失函数。该损失函数有助于缓解类别不平衡问题，并引导网络在训练过程中更加关注困难目标区域及边缘特征。此外，我们提出了一种用于后处理的可调灵敏度策略。该策略在保证分割精度的同时，显著提升了红外弱小目标的检出率。大量实验结果表明，所提方案取得了最佳性能。值得注意的是，该方案在PRCV 2024广域红外弱小目标检测竞赛中荣获一等奖。