基于目标特征分布增强卷积神经网络的红外目标检测算法

Infrared Target Detection Algorithm Based on Target Feature Distribution Enhanced Convolutional Neural Network

为了实现对水上红外弱小目标的探测,并减少由红外图像信噪比低、目标与背景的红外特征差异小等问题对检测结果的影响,通过结合红外图像的成分、纹理及目标形状特征,提出了基于目标特征分布增强卷积神经网络的红外目标检测(TFD_CNN)算法。该算法包含目标特征分布学习与深度神经网络,具备滤除红外图像中噪声的能力,并深度挖掘红外图像中目标的边缘、纹理及形状信息,提升了卷积神经网络的分类精度。通过与4种算法进行实验对比,TFD_CNN算法分类准确率为96%,高于其他算法。结果表明:TFD_CNN算法具备对红外图像中落水人员与船只的分类能力。

In order to achieve the detection of infrared weak small maritime targets and reduce the impact of low signal-to-noise ratio of infrared images and small differences in infrared features between targets and backgrounds on the detection results,a infrared target detection algorithm based on target feature distribution enhanced convolutional neural network(TFD_CNN)is proposed by combining the composition,texture,and target shape features of infrared images.The algorithm includes target feature distribution learning and deep neural networks,which has the ability to filter out noise in infrared images and deeply mines the edge,texture and shape information of the target in the infrared image to improve the classification accuracy of the convolutional neural network.Through the experimental comparison with four other algorithms,the classification accuracy of the TFD_CNN algorithm is 96%,which is higher than other algorithms.The results show that the TFD_CNN algorithm has the ability to classify drowning persons and ships in the infrared image.