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Abnormal Noise Recognition of Door Closing for Passenger Car based on Image Processing

[ Vol. 14 , Issue. 4 ]

Author(s):

Yongchao Song, Lili Su* and Xiaolan Wang   Pages 505 - 514 ( 10 )

Abstract:


Background: With the rapid development of automobile technology, the problem of abnormal door-closing noise has attracted more and more attention. The abnormal door-closing noise is an important factor for judging the quality of a car, so accurate identification of abnormal noise is the premise of fundamentally solving the abnormal noise.

Objective: To accurately identify the abnormal sound of car closing through the image processing method.

Methods: To accurately identify the abnormal noise of car closing the method to recognize and classify images using Support Vector Machine (SVM) is proposed. This method extracts Histogram of Oriented Gradient (HOG), texture and Speed Up Robust Features (SURF). The three extracted feature vectors are combined and used as the input of SVM. The classifiers obtained by different kernel functions are used to predict the labels of the test set, respectively.

Results: Calculating the ratio of the value on the diagonal of the confusion matrix to the total number of each row, and this ratio is the classification accuracy rate. Test accuracy rate is 85%, the results indicate that the accuracy is high.

Conclusion: This paper uses image processing methods to extract HOG, GLCM, SURF features and merge them together as a new feature vector. The experimental results show that the SVM classifier using the Gaussian kernel function optimized by hyperparameters has a high accuracy rate and can be used to identify whether the door is closed with abnormal noise.

Keywords:

Abnormal sound recognition, HOG feature, Kernel function, support vector machine, SURF feature, texture feature.

Affiliation:

School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201600, School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201600, School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201600



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