摘要
运用神经网络图像特征提取联合SSA-SVM分类算法,对通航区域图像中的典型船舶目标进行识别以实现船舶目标的自动分类。首先通过摄像机获得通航区域的高分辨率图像,以AlexNet深度学习网络为基础经迁移学习后提取典型船舶目标特征,获得4种船舶类型、共5 505 024个特征数的典型船舶目标特征矩阵。以特征矩阵为训练依据训练SSA-SVM算法,在种群寻优下获得最佳识别参数,经训练得出在小数据集下具有较强辨识能力的SSA-SVM船舶目标识别模型。实验表明,相比于深度学习的大数据集驱动识别算法,使用AlexNet特征提取的SSASVM算法能够在数据量较少的情况下对散货船、集装箱船等典型船舶目标进行有效识别,识别准确率为88.87%、训练时长为1 856 s,满足实用需求,为水上监管提供了可靠的技术支持。
The neural network image feature extraction combined with SSA-SVM classification algorithm is used to identify typical ship targets in the navigable area images to achieve automatic classification of ship targets.Firstly,the highresolution image of the navigable area is obtained by the camera,and the typical ship target characteristics matrix of 4 types of ship types and 5505024 features are obtained after transfer learning based on the AlexNet deep learning network.Based on the feature matrix,the SSA-SVM algorithm is trained,the optimal identification parameters are obtained under the population optimization,and the SSA-SVM ship target recognition model with strong recognition ability under the small data set is trained.Experiments show that compared with the large dataset-driven recognition algorithm of deep learning,the SSASVM algorithm using AlexNet feature extraction can effectively identify typical ship targets such as bulk carriers and container ships with less data volume,with an identification accuracy of 88.87%and a training time of 1856 s,which meets practical needs and provides reliable technical support for water supervision.
作者
马玉鹏
郑茂
吴勇
徐海潮
MA Yu-peng;ZHENG Mao;WU Yong;XU Hai-chao(School of Transportation and Logistics Engineering,Wuhan University of Technology,Wuhan 430000,China;National Engineering Research Center for Water Transport Safety,Wuhan University of Technology,Wuhan 430000,China;School of Physics and Electronic Information Engineering,Minjiang University,Fuzhou 350108,China;Huashi Design Group Co.,Ltd.,Nanjing 210014,China)
出处
《舰船科学技术》
北大核心
2023年第8期158-164,共7页
Ship Science and Technology
基金
国家自然科学基金资助项目(52001243,52172327)
集美大学航海学院—船舶辅助导航技术国家地方联合工程研究中心开放基金资助项目(JMCBZD202005)
工信部高技术船舶研发专项(MC-201920-X01)。
