基于遗传算法优化支持向量机的船舰目标识别分类  

Ship target recognition and classification based on genetic algorithm optimization of support vector machine

作　　者：杨永平 YANG Yong-ping(School of Information Technology,Beijing Normal University,Zhuhai 519087,China)

机构地区：[1]北京师范大学珠海分校信息技术学院,广东珠海519087

出　　处：《舰船科学技术》2024年第4期174-178,共5页Ship Science and Technology

摘　　要：为了实现有效的海上监管和响应,提高舰船监管效率,降低人力成本,提出基于遗传算法优化支持向量机的舰船目标识别分类方法。以HU矩为舰船目标的特征描述子,在舰船目标图像内,提取具备旋转、尺度与平移不变性的舰船目标特征矩;利用遗传算法,优化支持向量机的惩罚因子与核参数;在参数优化后的支持向量机内,输入舰船目标特征矩样本,输出舰船目标识别分类结果。实验证明,该方法可有效提取舰船目标特征矩;经过参数优化后的支持向量机,可有效降低计算复杂度,加快检测目标识别分类效率,具备较优的舰船目标识别分类性能。该方法均可精准识别分类舰船目标。In order to realize effective maritime supervision and response,improve ship supervision efficiency and reduce labor cost,the ship target recognition and classification method of genetic algorithm optimization support vector machine is studied.Taking HU moment as the characteristic descriptor of ship target,the characteristic moment of ship target with rotation,scale and translation invariance is extracted from ship target image.The penalty factor and kernel parameters of sVM are optimized by genetic algorithm.In the support vector machine after parameter optimization,the characteristic moment samples of ship target are input and the recognition and classification results of ship target are output.Experimental results show that this method can extract the characteristic moments of ship target effectively.After parameter optimization,support vector machine can effectively reduce the computational complexity,speed up the detection target recognition and classification efficiency,and has better ship target recognition and classification performance.This method can accurately identify and classify ship targets.

关 键 词：遗传算法 支持向量机 舰船目标 识别分类 HU矩 特征描述子 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]