深度神经网络在螺旋桨叶片数识别中的应用

从调制(Demodulation on Noise, DEMON)谱谐波簇中提取的结构特征可以建立用于螺旋桨叶片数识别的模板。使用模板匹配算法进行螺旋桨叶片数识别时,存在依赖模板库和置信度准则、算法约束条件多、无法发现缺失模板等问题。本文提出了一种将深度神经网络(Deep Neural Network, DNN)应用于螺旋桨叶片数识别的方法,该方法仅在训练深度神经网络时使用模板库,克服了识别过程中对模板库和置信度准则的依赖。此外,通过提取识别错误项,可以找到缺失模板,实现了对模板库数据的补充。使用该算法对大量实测数据进行检测,发现深度神经网络具有更高的识别正确率,而且识别过程更加简单可靠。

Application of deep neural network in blade-number recognition of ship propeller

Structural feature vectors, which extracted from the harmonic waves in DEMON spectral, can be used to establish the templates for recognizing the propeller blade-number of ship propeller. However, in the recognition method based on template matching algorithm, there are some problems hard to be solved, such as relying on template library and confidence factor algorithm, containing too many constraints and unable to find missing templates. In this paper, a Deep Neural Network (DNN) based method for propeller blade-number recognition is proposed. In this method, the template library is only used when training the deep neural network, so that the problem of relying on template library and confidence factor algorithm disappears in the recognition process. In addition, by extracting the recognition error item, the missing templates can be found as the supplement of the template library. Through the tests of propeller blade-number recognition from the measured large amount of ship radiated noise data, it is confirmed that the DNN based method has higher accuracy in propeller blade-number recognition, and the recognition process is more simple and reliable.