Infrared small target detection algorithm for UAV detection system
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摘要: 为了解决无人机探测系统中目标检测算法在不同场景下适用性差、虚警率高的问题, 采用可应用于不同复杂背景的红外小目标检测算法, 设计了一种基于现场可编程门阵列与数字信号处理器架构的无人机探测系统。首先利用双边滤波算法平滑背景, 保留目标区域边缘; 再使用改进的多尺度顶帽算法进行目标增强和背景抑制, 来提高目标区域与周围区域的差异对比; 最后使用基于最大值和平均值的自适应阈值分割方法提取目标。结果表明, 实验测得系统的检测率为98.15%, 整体时延为33.33 ms, 与现有典型红外小目标检测算法相比, 该算法的信噪比增益和背景抑制因子分别平均提高6.8倍和7.44倍, 有效地抑制了背景, 增强了目标。该算法能有效解决复杂背景下的红外小目标检测问题, 对提高无人机探测系统在不同场景下的适用能力与检测能力是有帮助的。
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关键词:
- 图像处理 /
- 小目标检测 /
- 双边滤波 /
- 改进top-hat算法 /
- 自适应阈值
Abstract: In order to solve the problem of poor applicability and high false alarm rate of target detection algorithm in unmanned aerial vehicle (UAV) detection systems in different scenarios, a UAV detection system based on a field-programmable gate array(FPGA) and digital signal processor architecture was designed by using infrared small target detection algorithm which could be applied to different complex backgrounds. Firstly, a bilateral filter algorithm was used to smooth the background and preserve the edge of the target region. Then, an improved multi-scale top-hat algorithm was adopted to enhance the target and suppress the background to improve the contrast difference between the target and the surrounding area. Finally, the adaptive threshold segmentation method based on maximum and average values was used to extract the target. The experimental results show that the detection rate of the system is 98.15%, and the overall delay is 33.33 ms. Compared with the existing typical infrared small target detection algorithms, the signal-to-noise ratio gain and background suppression factor of this proposed algorithm are increased by 6.8 times and 7.44 times on average, respectively, which effectively suppresses the background and enhances the target. The algorithm can effectively solve the problem of infrared small target detection in complex backgrounds, and it is helpful to improve the applicable ability and detection ability of the UAV detection system in different scenarios. -
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表 1 序列图像描述
Table 1 Sequential image description
image sequence image size target number target size image background (random background noise and system interference) Seq.1 256×256 1 3×3 complex cloud background Seq.2 256×256 1 3×3 mountain background Seq.3 256×256 1 3×3 forest background Seq.4 256×256 2 3×3 sky multiple targets 
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表 2 5种算法在前3组场景下的SNRG与BSF值
Table 2 Sequence images describe the SNRG and BSF values of the five algorithms in the first three sets of scenes
evaluation index image sequence LCM top-hat MPCM LoG this paper SNRG Seq.1 7.78 231.21 112.09 317.64 1517.63 Seq.2 0.38 2.61 9.85 8.04 38.79 Seq.3 0.05 2.29 9.04 5.15 16.21 BSF Seq.1 0.98 6.73 0.50 7.36 26.53 Seq.2 0.93 2.07 2.81 2.94 25.72 Seq.3 0.83 1.42 3.43 1.56 6.79
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