Indistinguishable points attention-aware network for infrared small object detection
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摘要:
随着飞行器机动性能的提升，多帧红外小目标检测方法不足以满足检测要求。近年来，基于深度学习的单帧红外小目标检测方法取得了巨大成功。然而，红外小目标通常缺少形状特征，而且边界与背景模糊不清，给准确分割带来了一定的挑战。针对上述问题，本文提出难点注意力感知红外小目标检测网络。通过基于点的区域建议模块获取目标潜在区域，同时滤除多余背景。为实现高质量分割、细化掩码边界模块、判断粗掩码中无序、非局部难以分辨点，融合这些难点的多尺度特征，进行逐像素注意力建模。最后，由点检测头对难点注意力感知特征重新预测，生成精细分割掩码。在公开数据集NUDT-SIRST和IRDST上进行测试，平均精度均值mAP达到87.4和63.4，F值达到0.8935和0.7056。本文提出的难点注意力感知红外小目标检测网络可在多检测场景、多目标形态下实现准确分割，抑制误报信息，同时控制计算开销。
Abstract: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.
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图 1 难点注意力感知网络结构图
Figure 1. Indistinguishable points attention-aware network architecture
图 2 (a)中心点补偿边界；(b)基于点的区域建议模块
Figure 2. (a) Centre point offset to boundary; (b) point-based region proposal module
图 3 各算法在(a) NUDT-SIRST 数据集和(b) IRDST 数据集 ROC 曲线
Figure 3. ROC curves for methods on (a) the NUDT-SIRST dataset and (b) the IRDST dataset
图 4 各方法不同场景检测结果，相同目标放大显示于同颜色框内
Figure 4. Different scene detection results of different methods, with the same target zoomed in the same color box
图 6 区域建议可视化结果。(a)原图；(b)真值；(c)中心点热图及区域建议边界
Figure 6. Visualization of regional proposals. (a) Original maps; (b) groundtruths; (c) centroid heatmap and regional proposal boundaries
图 7 难点分布可视化。(a)无人机目标；(b)点目标；(c)飞机目标
Figure 7. Visualization of indistinguishable points distribution. (a) UAV target; (b) point target; (c) aircraft target
表 1 传统算法超参数设置
Table 1. Hyperparameter settings of traditional algorithms
传统算法 超参数设置 Top-hat Nhood=ones(5) LEF h=0.2，α=0.5， P=9 AADCDD 内窗口尺寸={3, 5, 7, 9}，外窗口尺寸=19 TLLCM 窗口尺寸={3, 5, 7, 9}，k=9 
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表 2 各方法在NUDT-SIRST及IRDST数据集定量结果对比
Table 2. Comparison of quantitative results of different methods on NUDT-SIRST and IRDST datasets
检测算法 NUDT-SIRST IRDST mAP F值（Pre，Rec） mAP F值（Pre，Rec） Top-hat 1.5 0.3599（0.2850，0.4884） 0.7 0.0088（0.0045，0.4107） LEF 6.4 0.1151（0.0748，0.2498） 2.5 0.1219（0.0686，0.5470） AADCDD 1.6 0.1490（0.3838，0.0924） 1.4 0.0705（0.0521，0.1090） TLLCM 16.5 0.0724（0.0479，0.1476） 6.1 0.1881（0.1254，0.3759） ALCNet 69.3 0.7595（0.7035，0.8251） 46.5 0.5929（0.5461，0.6486） DNANet 86.9 0.8645（0.9070，0.8259） 62.1 0.6697（0.712 4，0.6319） RDIAN 82.4 0.890 0（0.899 0，0.881 1） 60.0 0.7102（0.7092，0.7113） 本文方法 87.4 0.8935（0.8923，0.8948） 63.4 0.7056（0.7183，0.6935）
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表 3 深度学习方法单张图片平均推理时间
Table 3. Average inference times of a single image for deep learning methods
(s) 检测算法 NUDT-SIRST IRDST ALCNet 0.104 0.166 DNANet 0.089 0.259 RDIAN 0.065 0.114 本文算法 0.099 0.121
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表 4 不同区域建议模块对比表
Table 4. Comparison of different region proposal modules
建议数量 基于点的区域建议 RPN mAP F值 mAP F值 1000 87.9 0.8927 86.2 0.8425 256 87.5 0.8962 85.8 0.8412 128 87.4 0.8935 85.2 0.8406 64 86.0 0.8901 84.5 0.8397
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表 5 不同选点策略检测结果
Table 5. Detection results of different point selection strategies
选点策略 mAP 均匀选点 86.7 k=1,γ=0.00 86.9 k=3,γ=0.75 87.4 k=10,γ=1.00 85.8
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表 6 难点不同特征融合结果
Table 6. Fusion results of different features at indistinguishable points
细粒度特征 粗糙掩码 位置嵌入 mAP √ 85.5 √ √ 85.8 √ √ √ 87.4
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表 7 不同细化方案检测结果
Table 7. Results of different refinement strategies
细化方案 mAP CNN（16×16） 85.5 MLP（16×16） 86.2 细化掩码边界模块（S=3） 87.4 细化掩码边界模块（S=6） 87.6
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