针对一般检测算法适应性差、抗干扰能力弱、在进行空中弱小目标检测时容易受到云层噪声或突变的干扰, 导致检测结果不精确的问题, 提出了一种基于信息融合的空中弱小目标检测算法。首先, 对图像进行降采样, 减少处理数据, 然后通过两种滤波方式对图像进行预处理, 接着用经典的质心检测和显著性检测进行模型叠加融合, 最后再结合所提出的基于帧间信息关联的检测方法, 将“类目标”点剔除, 从而进一步提升小目标检测算法精度, 增强检测算法的抗干扰性。通过仿真实验和对比实验对算法进行了验证, 实验结果表明, 所提算法检测效果更好, 算法抗干扰性更强。

The traditional detection algorithms have poor adaptability and weak anti-interference ability, which are vulnerable to the interference of the noise of clouds or mutation in dim target detection, and may lead to inaccurate detection results. To solve the problem, this paper puts forward an algorithm for dim target detection in the air based on information fusion. First of all, the image down-sampling is carried out to reduce the processing data, and the image is pre-processed by two kinds of filtering methods. Then, the traditional centroid detection and saliency detection are fused by model superposition. Finally, with the detection method based on the inter-frame information correlation in this paper, the “target-like” point is eliminated, so as to further improve the accuracy of the detection algorithm for small targets, and enhance the anti-interference ability of the detection algorithm. The algorithm is verified by simulations and contrast experiments. The experimental results show that the proposed algorithm has better detection effects and a stronger anti-interference ability.