CAJ | 학술논문

本文利用BP神经网络抗干扰性强，识别精准等优点对船舶进行识别跟踪。首先获取原始图像，然后预处理，以图像的全部灰度值为训练样本，以新不变矩特征向量为样本集输入到3层BP神经网络中，对含不同噪声均值的图像进行识别。实验结果表明，以新不变矩特征向量作为样本集时抗噪能力强，识别率高。最后以新不变矩特征向量作为样本集进行目标跟踪得到跟踪误差。
본문이용BP신경망락항간우성강，식별정준등우점대선박진행식별근종。수선획취원시도상，연후예처리，이도상적전부회도치위훈련양본，이신불변구특정향량위양본집수입도3층BP신경망락중，대함불동조성균치적도상진행식별。실험결과표명，이신불변구특정향량작위양본집시항조능력강，식별솔고。최후이신불변구특정향량작위양본집진행목표근종득도근종오차。
In this paper, because of strong interference and precise identification, etc. , use BP neural network to identification and tracking of ships. First, get the original image, then pretreated. Respectively, all of grayscale images is training samples and a new moment invariant feature vector for the sample set input to the BP neural network. Use BP neural network to identify images with different noise. Experimental results show that when a new moment invariant feature vectors as the sample set has strong noise immunity and high recognition rate. Finally, a new moment invariant feature vectors as the sample set for target tracking to get the tracking error.