基于遗传算法优化支持向量机的超声图像缺陷分类

超声图像缺陷在分类时由于存在样本数量少、样本类别多、不易区分等问题,分类的准确率较低。针对这些问题,提出了基于遗传算法优化支持向量机的超声图像缺陷分类方法。该方法首先通过图像处理提取超声图像缺陷的特征数据,然后训练支持向量机作为超声图像缺陷分类器,最后采用遗传算法优化参数求得最优的分类器。实验结果表明,提出的超声图像缺陷分类器在识别率方面优于其他方法的分类器,综合识别率达到了90%,可以有效地辅助工作人员对超声图像缺陷进行分类识别。

Ultrasonic Image Defect Classification Based on Support Vector Machine Optimized by Genetic Algorithm

Ultrasound image defects are classified at a low accuracy due to problems such as small sample size, large sample categories, and difficulty in distinguishing. Aiming at these problems, an ultrasonic image defect classification method based on support vector machine optimized by genetic algorithm was proposed. First, the feature data of ultrasonic image defect is extracted by image processing. Then, the support vector machine was trained as ultrasonic image defect classifiers. Finally the parameters of the classifiers were optimized by genetic algorithm to obtain the optimal classifiers. The experimental results show that the proposed ultrasonic image defect classifier is superior to the other methods in the recognition rate, and the comprehensive recognition rate reaches 90%, which can effectively assist the staff to classify and identify the ultrasonic image defects.