无人机小样本条件下遮挡和混淆目标识别方法

针对无人机对地目标识别过程中的小样本问题以及目标存在的遮挡和混淆情况,提出了一种融合自注意力机制的小样本目标识别模型。在利用元学习思想获取小样本学习能力的基础上,将自注意力机制学习目标内部各部分之间的上下文依赖关系引入模型,从而增强目标表征能力,以解决遮挡和混淆情况下有效特征不足的难题。为验证模型效果,通过对基准数据集和无人机航拍数据进一步加工,构建了遮挡和混淆目标数据集,设置了不同的遮挡程度和背景混淆率。通过在不同数据集上的验证,并与深度学习模型对比,证明提出的模型具有更高的学习效率和识别正确率。

Occlusion and confusion targets recognition method for UAV under small sample conditions

Aiming at the problem of occlusion and confusion targets recognition for UAV (unmanned air vehicle) under small sample conditions, a target recognition model integrating self-attention mechanism and few-shot learning was proposed. On the basis of using the idea of meta learning to obtain the ability of few-shot learning, the self-attention mechanism to learn the context dependence between the internal parts of the target was introduced into the model, so as to enhance the target representation ability and solve the problem of insufficient effective features in the case of occlusion and confusion. In order to verify the effect of the model, the occlusion and confusion target datasets were constructed by further processing the reference datasets and UAV aerial photography data, and different occlusion degrees and background confusion rates were set. Through the verification on different datasets and compared with the deep learning model, the proposed model is proved to possess higher learning efficiency and recognition accuracy.