  Recently, infrared small target detection with single-point supervision has
attracted extensive attention. However, the detection accuracy of existing
methods has difficulty meeting actual needs. Therefore, we propose an
innovative refined infrared small target detection scheme with single-point
supervision, which has excellent segmentation accuracy and detection rate.
Specifically, we introduce label evolution with single point supervision
(LESPS) framework and explore the performance of various excellent infrared
small target detection networks based on this framework. Meanwhile, to improve
the comprehensive performance, we construct a complete post-processing
strategy. On the one hand, to improve the segmentation accuracy, we use a
combination of test-time augmentation (TTA) and conditional random field (CRF)
for post-processing. On the other hand, to improve the detection rate, we
introduce an adjustable sensitivity (AS) strategy for post-processing, which
fully considers the advantages of multiple detection results and reasonably
adds some areas with low confidence to the fine segmentation image in the form
of centroid points. In addition, to further improve the performance and explore
the characteristics of this task, on the one hand, we construct and find that a
multi-stage loss is helpful for fine-grained detection. On the other hand, we
find that a reasonable sliding window cropping strategy for test samples has
better performance for actual multi-size samples. Extensive experimental
results show that the proposed scheme achieves state-of-the-art (SOTA)
performance. Notably, the proposed scheme won the third place in the "ICPR 2024
Resource-Limited Infrared Small Target Detection Challenge Track 1: Weakly
Supervised Infrared Small Target Detection".
