深度学习的空间红外弱小目标状态感知方法

针对当前空间红外弱小目标状态感知方法存在判别准确率低、人工干涉较多、对数据质量要求较高等问题,提出了一种全新的基于深度学习的判别算法。首先,对空间红外弱小目标状态变化进行了分析,并建立了专用数据集;然后,建立了目标状态感知任务专用的卷积神经网络框架,并在局部标注及自适应阈值等方面进行了创新;最后,应用实验室采集的目标辐射强度信息制作的仿真数据对本算法进行了训练和测试,建立了目标状态感知评估指标体系,并对实验结果进行评估。实验结果表明:在输入连续完整的辐射强度信息时,判别准确率为98.27%;输入片段辐射强度信息时,各状态判别准确率皆大于90%。本算法弥补了现有方法对空间弱小目标状态感知虚警率高和目标信息不完整时不敏感的缺陷,提高了检测速度和精度,可以更好地满足空间红外弱小目标感知任务的需求。

A state perception method for infrared dim and small targets with deep learning

Aiming at the problems of low accuracy,high artificial interference and high data quality requirements of the current spatial infrared dim target state perception,a new deep learning-based discrimination algorithm is proposed.Firstly,the state change of weak spatial infrared dim target is analyzed and a special data set is established.Then,a convolutional neural network dedicated to target state perception is established and adjustments are made in its local annotations and adaptive threshold.Finally,simulation data is generated from the target's radiation intensity information that was collected in the laboratory and is used to train and test the algorithm.A target state perception evaluation indexing system is established to evaluate the experimental results.The experimental results show that the accuracy of this method is 98.27%when the continuous complete radiation intensity information is inputted.When the radiation intensity information of the segment is inputted,the accuracy of each state is greater than 90%.This algorithm makes up for the short-comings of current methods,which are not sensitive to low false alarm rates and incomplete target information.It improves detection speed and accuracy and better satisfies the demand for spatial infrared weak target sensing tasks.