| Citation: | WANG Bo-xiao, SONG Yan-song, DONG Xiao-na. Indistinguishable points attention-aware network for infrared small object detection[J]. Chinese Optics. doi: 10.37188/CO.2023-0178
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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 methods based on deep learning; however, infrared small targets often lack shape features and have blurred boundaries and backgrounds, obstructing accurate segmentation. Based on this, 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 fine mask boundary module determined disordered, non-local indistinguishable points in the coarse mask, fused multi-scale features, and modeled the attention pixel by pixel; finally, the point detection head generated a fine segmentation mask by re-predicting the indistinguishable points’ attention-aware features. The proposed method reached 87.4 mAP and 63.4 mAP 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.
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