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 this https URL

使用更有效但结构简单的损失函数提高检测性能，通过引入基于目标尺度的权重和基于目标中心点的惩罚项处理现有损失的局限性，并设计了一个简单的多尺度头部结构，通过在每个预测尺度上应用 Scale and Location Sensitive (SLS) 损失函数，超过现有最先进方法的检测性能。