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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers >Investigation of infrared dim and small target detection algorithm based on the visual saliency feature
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Investigation of infrared dim and small target detection algorithm based on the visual saliency feature

机译：基于视觉显着特征的红外暗淡和小目标检测算法研究

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摘要

Infrared dim and small target detection has an important role in the infrared thermal imaging seeker, infrared search and tracking system, space-based infrared system and other applications. Inspired by human visual system (HVS), based on the fusion of significant features of targets, the present study proposes an infrared dim and small target detection algorithm for complex backgrounds. Firstly, in order to calculate the target saliency map, the proposed algorithm initially uses the difference of Gaussian (DoG) and the contourlet filters for the preprocessing and fusion, respectively. Then the multi-scale improved local contrast measure (ILCM) method is applied to obtain the interested target area, effectively suppress the background clutter and improve the target signal-to-clutter ratio. Secondly, the optical flow method is used to estimate motion regions in the saliency map, which matches with the interested target region to achieve the initial target screening. Finally, in order to reduce the false alarm rate, forward pipeline filtering and optical flow estimation information are applied to achieve the multi-frame target recognition and achieve continuous detection of dim and small targets in image sequences. Experimental results show that compared with the conventional Tophat (TOP-HAT) and ILCM algorithms, the proposed algorithm can achieve stable, continuous and adaptive target detection for multiple backgrounds. The area under curve (AUC) and the harmonic average measure F1 are used to measure the overall performance and optimal performance of the target detection effect. For sky, sea and ground backgrounds, the test results of proposed algorithm for most sequences are 1. It is concluded that the proposed algorithm significantly improves the detection effect.
机译：红外暗淡和小目标检测在红外线热成像搜索器，红外搜索和跟踪系统，基于空间的红外系统等应用中具有重要作用。通过人类视觉系统（HVS）的启发，基于目标的重要特征，本研究提出了一种用于复杂背景的红外暗淡和小目标检测算法。首先，为了计算目标显着图，所提出的算法最初使用高斯（狗）和Contourlet滤波​​器的差异分别用于预处理和融合。然后，应用多尺度改进的局部对比度测量（ILCM）方法以获得感兴趣的目标区域，有效地抑制背景杂波并提高目标信号到杂波比。其次，光学流量方法用于估计显着图中的运动区域，其与感兴趣的目标区域匹配以实现初始目标筛选。最后，为了降低误报率，向前施加流水流滤波和光学流量估计信息以实现多帧目标识别并实现图像序列中的昏暗和小目标的连续检测。实验结果表明，与传统的Tophat（顶帽）和ILCM算法相比，该算法可以实现多个背景的稳定，连续和自适应的目标检测。曲线（AUC）下的区域和谐波平均度量F1用于测量目标检测效果的整体性能和最佳性能。对于天空，海洋和地面背景，大多数序列所提出的算法的测试结果是1.得出结论，所提出的算法显着提高了检测效果。

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