Abstract
The speed and efficiency of overloaded artificial screening bus images are relatively low, which results in a large number of human resources waste problems. Therefore, an overload classification method for bus images based on image processing and support vector machine was proposed to intelligently identify the image overload or not. Based on the consideration we have done the following work. Firstly, the bus images were preprocessed, including image enhancement using histogram equalization method and image segmentation using improved Otsu algorithm; Secondly, the features of the segmented images was extracted by Kirsch edge detection operator to establish the image feature sample library; Finally, the appropriate kernel function and parameters were chosen to establish a classifier model based on support vector machine, which can train the sample library to classify the bus images. Theoretical analysis and experimental results show that the average classification accuracy of the polynomial kernel function is better than those of the Gaussian kernel function and the Sigmoid kernel function in the finite range of parameters selection. When the parameter d of the polynomial kernel function is 4, the classification accuracy is 93.68%, and its classification performance is stable and there is no significant increase or fall. And the conclusion was verified in the actual application.
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Li, T., Sun, Y., Liang, Y., Zhai, Y., Ji, X. (2018). Research on Overload Classification Method for Bus Images Based on Image Processing and SVM. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11336. Springer, Cham. https://doi.org/10.1007/978-3-030-05057-3_3
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DOI: https://doi.org/10.1007/978-3-030-05057-3_3
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