面向目标识别的特征融合模糊模型及其应用  

Fusion Multi-feature Fuzzy Model for Target Recognition and Its Application

作　　者：阮旺 郝国生[1] 王霞[1] 胡晓婷[1] 杨子豪 RUAN Wang;HAO Guosheng;WANG Xia;HU Xiaoting;YANG Zihao(School of Computer Science and Technology,Jiangsu Normal University,Xuzhou,Jiangsu 221116,China)

机构地区：[1]江苏师范大学计算机科学与技术学院,江苏徐州221116

出　　处：《计算机科学》2023年第S01期495-501,共7页Computer Science

基　　金：国家自然科学基金(61673196);徐州市科技计划项目(KC19213);江苏省研究生科研与实践创新计划项目(KYCX21_2633);赛尔网络下一代互联网技术创新项目(NGⅡ20190513)。

摘　　要：针对在自然识别场景下,图像特征往往具有复杂性、多样性和模糊性的特点,以及在利用多个特征对图像进行识别时,往往缺乏考虑特征间的关系等问题,提出了一种融合多种图像特征的目标识别模糊模型。首先,对图像进行特征提取,将特征的取值作为模型的模糊集并给出对应的隶属函数;其次,给出模型的评价指标,根据指标论证模型的可行性;然后,利用粒子群优化算法对图像各特征的隶属函数的参数进行优化;最后,给出基于特征融合模糊模型的目标识别算法,并将算法应用于填涂点识别与热轧带钢表面缺陷判别这两个识别场景来进行实验论证。实验结果表明,所设计的模型在评价指标下表现良好,算法明显提高了目标识别的准确率与鲁棒性以及改善了特征融合的合理性。In natural recognition scenes,image features are often characterized by complexity,diversity and fuzziness,and lack of consideration of the relationship between features when using multiple features for image recognition,a target recognition fuzzy model integrating multiple image features is proposed.Firstly,the image feature is extracted,the value of the feature is taken as the fuzzy set of the model,and the corresponding membership function is given.Secondly,the evaluation index of the model is gi-ven,and the feasibility of the model is demonstrated according to the index.Thirdly,particle swarm optimization algorithm is used to optimize the parameters of membership function of image features.Finally,the target recognition algorithm based on feature fusion fuzzy model is proposed,which is applied to filling-mark recognition and the hot rolled strip surface defect recognition.Experimental results show that the designed model performs well under the evaluation index,and the algorithm significantly improves the accuracy and robustness of target recognition and the rationality of feature fusion.

关 键 词：目标识别 特征融合 模糊数学 隶属函数 粒子群优化算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]