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| Ultrasonic Image Defect Classification Based on Support Vector Machine Optimized by Genetic Algorithm |
| ZHANG Mo,ZHENG Hui-feng,NI Hao,WANG Yue-bing,GUO Cheng-cheng |
| College of Metrology & Measurement Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China |
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Abstract 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.
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Received: 28 October 2018
Published: 01 September 2019
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Corresponding Authors:
Hui-feng ZHENG
E-mail: zjufighter@cjlu.edu.cn
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2019, Vol. 40 
