As aircraft maneuverability increases, multi-frame infrared small target detection methods are becoming insufficient to meet detection requirements. In recent years, significant progress has been achieved in single-frame infrared small-target detection method based on deep learning. However, infrared small targets often lack shape features and have blurred boundaries and backgrounds, obstructing accurate segmentation. According to the problems, an indistinguishable points attention-aware network for infrared small object detection was proposed. First, potential target areas were acquired through a point-based region proposal module while filtering out redundant backgrounds. Then, to achieve high-quality segmentation, the mask boundary refinement module was utilized to identify disordered, non-local indistinguishable points in the coarse mask. Multi-scale features of these difficult points were then fused to perform pixel-wise attention modeling. Finally, A fine segmentation mask was generated through re-predicting the indistinguishable points attention-aware features by point detection head. The mAP of the proposed method reached 87.4 and 63.4 on the publicly available datasets NUDT-SIRST and IRDST, and the F-measure reached 0.8935 and 0.7056, respectively. It can achieve accurate segmentation in multi-detection scenarios and multi-target morphology, suppressing false alarm information while controlling the computational overhead.