基于SAE与底层视觉特征融合的无人机目标识别算法(英文)

UAV target recognition algorithm based on fusion of SAE and bottom visual feature

无人机在复杂战场环境下,因敌方无人机外形、颜色等特征较为相似,现有基于底层视觉特征无法快速地对其进而准确的识别,从而造成误检测甚至误打击等事件的发生。针对这一问题,文中提出基于稀疏自动编码器融合底层视觉特征的算法,对无人机目标对象进行识别。算法首先利用底层视觉特征描述子(GIST、LBP)以及稀疏自动编码器(Sparse Auto-Encoder,SAE)提取目标对象的底层视觉特征和高层视觉特征;然后,采用主成分分析(PAC)法对全局特征进行降维融合;最后,将全局特征响应送入softmax回归模型完成无人机目标对象的分类。实验表明,与传统SAE算法及传统基于底层视觉特征描述子识别算法相比,新算法具有更高的准确性及鲁棒性。

UAV flying in complex battlefield environment, due to the similar shape and color of the enemy UAV, and the existing algorithms can not accurately identify and classify the UAV of the enemy,resulting in false detection or even mishandling attack. To solve this problem, a feature fusion algorithm based on the combination of the bottom visual features and high-level visual features was proposed to classify the UAV target objects. The algorithm first extracted the underlying visual features and high-level visual features of the target object by using visual feature descriptors and Sparse Auto-Encoder(SAE).Then, the principal component analysis(PAC) method was used to reduce the dimensionality of the global features. Finally, the global feature response was sent to the softmax regression model to complete the recognition and classification of the target object of the UAV. Experiments show that the new algorithm has higher accuracy and robustness compared with the traditional SAE algorithm and the traditional recognition algorithm based on the underlying visual features.