  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.
