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Pavement crack detection method and evaluation methods based on random forest

A pavement crack and random forest technology, applied in computer parts, image data processing, instruments, etc., can solve problems such as unsatisfactory effects, inability to represent cracks well, ignoring local structural information, etc., and achieve high crack detection accuracy. , the effect of increasing flexibility and versatility, and increasing computing speed

Inactive Publication Date: 2018-09-11
SHAANXI NORMAL UNIV
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Problems solved by technology

However, these methods are not ideal for the treatment of complex cracks
One possible reason is that the features used are based on grayscale information, which cannot represent some special cracks well
Moreover, these existing methods ignore the local structural information

Method used

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  • Pavement crack detection method and evaluation methods based on random forest
  • Pavement crack detection method and evaluation methods based on random forest
  • Pavement crack detection method and evaluation methods based on random forest

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Embodiment Construction

[0076] The present invention will be described in further detail below in conjunction with specific examples, but the embodiments of the present invention are not limited thereto.

[0077] In order to overcome the defects of the above methods, the present invention proposes a new road crack detection method based on random structure forest, which is called CrackForest. CrackForest combines complementary features of different scales to extract crack features and takes full advantage of the structural properties of cracks. Specifically, the present invention redefines the fracture segmentation template for the first time by introducing integral channel features. The fracture segmentation template contains structured information, thereby further expanding the traditional fracture detection feature set. Then, a random structure forest is utilized to learn this structural information and predict crack segmentation templates, which form the initial crack detection results. These st...

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Abstract

The invention relates to pavement crack detection and evaluation methods based on a random forest, and the detection method comprises the following steps: firstly extracting features for Chi, and thentraining Chi through a random structure forest, wherein all the templates together form a template structure space; carrying out the binarization processing and expansion and erosion of an image block; secondly describing all the templates in the template structure space through new crack descriptors, and inputting the description features into a classifier for learning; and finally inputting anoriginal image into the classifier for classification, and recognizing the image block with a crack, wherein the image block with the crack is set as a binarization result corresponding to the image block, and an image block with no crack is set to white 0. The detection method can achieve the effective detection of the complex cracks of a pavement structure, avoids the impact from noise, and is high in crack detection accuracy. The evaluation method is used for the image block, achieves the judgment of the crack conditions of a pavement, and achieves the discovery of the potential safety hazards of the pavement.

Description

technical field [0001] The invention belongs to the technical field of digital image processing and machine learning, and in particular relates to a random forest-based pavement crack detection method and an evaluation method thereof. Background technique [0002] Cracks are a road ailment that degrades road performance and threatens road safety. Government departments have done their best to build high-quality road networks, and they have now fully realized the importance of road inspection and maintenance systems. Crack detection is an important component of road maintenance systems and has attracted increasing attention in recent years. [0003] Cracks are an increasing threat to road safety and are also an urgent problem to be solved in intelligent transportation systems. However, the automatic detection of pavement cracks, as a key part of intelligent transportation systems, faces great challenges due to the irregularity of cracks, the complexity of crack topology, th...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06T5/30G06T5/40
CPCG06T5/30G06T5/40G06T2207/30168G06F18/2411G06F18/24147G06F18/24323
Inventor 李良福高小小孙瑞赟
Owner SHAANXI NORMAL UNIV
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