Recently, infrared small target detection (IRSTD) has been dominated by deep-learning-based methods. However, these methods mainly focus on the design of complex model structures to extract discriminative features, leaving the loss functions for IRSTD under-explored. For example, the widely used Intersection over Union (IoU) and Dice losses lack sensitivity to the scales and locations of targets, limiting the detection performance of detectors. In this paper, we focus on boosting detection performance with a more effective loss but a simpler model structure. Specifically, we first propose a novel Scale and Location Sensitive (SLS) loss to handle the limitations of existing losses: 1) for scale sensitivity, we compute a weight for the IoU loss based on target scales to help the detector distinguish targets with different scales: 2) for location sensitivity, we introduce a penalty term based on the center points of targets to help the detector localize targets more precisely. Then, we design a simple Multi-Scale Head to the plain U-Net (MSHNet). By applying SLS loss to each scale of the predictions, our MSHNet outperforms existing state-of-the-art methods by a large margin. In addition, the detection performance of existing detectors can be further improved when trained with our SLS loss, demonstrating the effectiveness and generalization of our SLS loss. The code is available at https://github.com/ying-fu/MSHNet.

翻译：近年来，基于深度学习的方法在红外小目标检测领域占据主导地位。然而，现有方法主要聚焦于设计复杂的模型结构以提取判别性特征，而对损失函数的研究相对不足。例如，广泛使用的交并比和Dice损失对目标的尺度与位置缺乏敏感性，限制了检测器的检测性能。本文致力于通过更高效的损失函数与更简洁的模型结构来提升检测性能。具体而言，我们首先提出一种新型的尺度与位置敏感损失函数，以克服现有损失函数的局限性：1）针对尺度敏感性，我们基于目标尺度对交并比损失进行加权计算，使检测器能够区分不同尺度的目标；2）针对位置敏感性，我们引入基于目标中心点的惩罚项，帮助检测器更精确地定位目标。随后，我们设计了一个简洁的多尺度头部结构，并将其应用于普通U-Net架构，形成MSHNet。通过将提出的损失函数应用于各尺度的预测结果，MSHNet大幅超越现有最优方法。此外，采用本文提出的损失函数训练现有检测器，其检测性能可进一步提升，这验证了该方法的有效性与泛化性。代码已开源至https://github.com/ying-fu/MSHNet。