王芳 , 王海晏 , 寇添 , 等 . 多维特征点空间的红外弱小目标检测方法 [J ] . 应用光学 , 2020 , 41 ( 6 ): 1268 - 1276 . DOI: 10.5768/JAO202041.0606002 http://doi.org/10.5768/JAO202041.0606002 将人工智能算法引入目标检测，空间红外弱小目标的检测也可归为模糊检测的二分类问题。依据空中红外弱小目标的探测模型，建立了信号电压比光谱模型，仿真分析表明电压比变化趋势与目标的速度、姿态和两机态势有关，可用以检测目标。采用动态特征构建理论，构建了红外弱小目标的双色比特征空间，基于该特征空间，优化最小二乘分类算法，用于从光谱信号层级检测目标。该方法不仅缩小了样本数据量，而且防止了高斯核函数参数选择引起的“过拟合”现象，既保证了分类精度，又使分类速率提高近1倍，为人工智能算法用于红外弱小目标检测提供了参考依据。

WANG F , WANG H Y , KOU T , et al . Multi-dimensional feature point space infrared dim target detection method [J ] . Journal of Applied Optics , 2020 , 41 ( 6 ): 1268 - 1276 . (in Chinese) DOI: 10.5768/JAO202041.0606002 http://doi.org/10.5768/JAO202041.0606002 As artificial intelligence algorithm was introduced into target detection, the detection of spatial infrared dim targets could be classified as the binary problem of fuzzy detection. According to the detection model of infrared dim target in the air, a signal voltage ratio spectrum model was established. The simulation analysis showed that the variation trend of voltage ratio was related to the speed, attitude of the target and the two-machine posture, which could be used to detect the target. The dynamic characteristics building theory was adopted to build the bicolor ratio feature space of infrared dim target. Based on this feature space, the least squares classification algorithm was optimized to identify the objects from the spectral signal hierarchy. This method not only reduces the amount of the sample data, but also prevents the phenomenon of over-fitting caused by the parameter selection of Gaussian kernel function. It ensures the classification accuracy and improves the classification efficiency nearly doubled. Reference basis is provided for infrared dim target detection by artificial intelligence algorithm.

牛畅 , 尹奎英 , 黄银和 . 无人机对地目标自动检测与跟踪技术 [J ] . 应用光学 , 2020 , 41 ( 6 ): 1153 - 1160 . DOI: 10.5768/JAO202041.0601003 http://doi.org/10.5768/JAO202041.0601003 针对无人机图像帧序列具有平台高速运动，视角旋转强烈，需要实时处理等特点，提出一种基于双级旋转不变特征空间检测（粗匹配-精细匹配）与并行特征提取跟踪的无人机对地目标图像帧序列自动快速目标检测与跟踪算法。采用图像子块的平均灰度值、灰度值方差、灰度值梯度构建特征空间。通过构造图像特征空间的方法来快速筛选待匹配图像的可疑区域，删除大量的背景区域，检测算法使用全局初步匹配加局部精细匹配的方法来规避算法复杂度的缺陷。理论及实验分析表明：该算法实时性强，对图像的旋转畸变具有抵消作用，对异常情况可以恰当处理，且全局初步匹配流程具有可移植性，可以在其他图像匹配跟踪算法中充当预处理器。实验结果表明：该算法在无人机对地的情况下可以保证对地面目标的稳定跟踪，配套检测算法具有较好的实时性，满足无人机图像目标检测跟踪实时处理的需要。

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