Learning-based road crack detection using gradient boost decision tree

Chronic hepatitis B (CHB) is a serious public health problem, and Traditional Chinese Medicine (TCM) plays an important role in the control and treatment for CHB. In the treatment of TCM, zheng discrimination is the most important step. In this paper, an approach based on CFS-GA (Correlation based Feature Selection and Genetic Algorithm) and C5.0 boost decision tree is used for zheng classification and progression in the TCM treatment of CHB. The CFS-GA performs better than the typical method of CFS. By CFS-GA, the acquired attribute subset is classified by C5.0 boost decision tree for TCM zheng classification of CHB, and C5.0 decision tree outperforms two typical decision trees of NBTree and REPTree on CFS-GA, CFS, and nonselection in comparison. Based on the critical indicators from C5.0 decision tree, important lab indicators in zheng progression are obtained by the method of stepwise discriminant analysis for expressing TCM zhengs in CHB, and alterations of the important indicators are also analyzed in zheng progression. In conclusion, all the three decision trees perform better on CFS-GA than on CFS and nonselection, and C5.0 decision tree outperforms the two typical decision trees both on attribute selection and nonselection.

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An enhanced real-time steel band wrinkling detection algorithm based on homomorphic filter and boost decision tree

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/493/1/012005 ◽

2019 ◽

Vol 493 ◽

pp. 012005

Author(s):

Sheng Xu ◽

Xiong Chen

Keyword(s):

Decision Tree ◽

Real Time ◽

Detection Algorithm ◽

Boost Decision Tree ◽

Steel Band ◽

Homomorphic Filter

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An Advanced Otsu Method Integrated with Edge Detection and Decision Tree for Crack Detection in Highway Transportation Infrastructure

Advances in Materials Science and Engineering ◽

10.1155/2021/9205509 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Haihang Han ◽

Hanyu Deng ◽

Qiao Dong ◽

Xingyu Gu ◽

Tianjie Zhang ◽

...

Keyword(s):

Image Segmentation ◽

Decision Tree ◽

Edge Detection ◽

Crack Detection ◽

Gaussian Function ◽

Decision Tree Classifier ◽

Pavement Maintenance ◽

Id3 Algorithm ◽

Tree Classifier ◽

Otsu Method

The detection of various cracks on pavement surfaces has drawn more and more attention from pavement maintenance engineers. In the traditional pavement image segmentation, due to the small area of the pavement cracks, the gray level of crack pixels only accounts for a very small portion in the grayscale histogram, making it difficult to segment. This paper developed an improved Otsu method integrated with edge detection and a decision tree classifier for cracking identification in asphalt pavements. An image preprocessing approach including Gaussian function-based spatial filtering and top-hat transform is firstly proposed to reduce the influence of poor shading and lighting effects significantly. Four edge detection operators including Prewitt, Sobel, Gauss–Laplace (LoG), and Canny are evaluated. The Canny edge detection has demonstrated outstanding performance in crack detection; this algorithm helps to obtain more details of both cracks and noises. The Sobel and LoG operators show similar image segmentation and retain fewer noises. The decision tree classifier based on the ID3 algorithm can effectively classify different types of cracks including transverse, longitudinal, and block ones.

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