Pavement Crack Detection and Recognition Method Based on Sparse Representation Classification

A technology of pavement cracks and sparse representation, applied in the fields of computer vision and pattern recognition, can solve the problems of long time and low detection accuracy

Active Publication Date: 2019-12-27
NANJING UNIV OF SCI & TECH
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Problems solved by technology

[0006] The purpose of the present invention is to provide a pavement crack detection and recognition method based on sparse representation classification to overcome the shortcomings of low detection accuracy and long time-consuming common in traditional methods

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  • Pavement Crack Detection and Recognition Method Based on Sparse Representation Classification
  • Pavement Crack Detection and Recognition Method Based on Sparse Representation Classification

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Embodiment Construction

[0023] In order to better understand the technical content of the present invention, specific embodiments are given together with the attached drawings for description as follows.

[0024] Aspects of the invention are described in this disclosure with reference to the accompanying drawings, which show a number of illustrated embodiments. Embodiments of the present disclosure are not necessarily intended to include all aspects of the invention. It should be appreciated that the various concepts and embodiments described above, as well as those described in more detail below, can be implemented in any of numerous ways, since the concepts and embodiments disclosed herein are not limited to any implementation. In addition, some aspects of the present disclosure may be used alone or in any suitable combination with other aspects of the present disclosure.

[0025] According to the embodiment of the present invention, the pavement crack detection and recognition method based on sp...

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Abstract

The present invention provides a pavement crack detection and identification method based on sparse representation classification. By introducing a sparse representation classifier (Sparse Representation-based Classifier, namely SRC), effective sub-block high-order moment features are selected to avoid image Pre-processing and post-processing are carried out to simplify the detection steps and improve the operating efficiency. Specifically, it includes: extraction and normalization of the feature vector of the training set (sub-block), extracting features from the test image into blocks and using SRC for classification, and identifying crack types according to the mapping code of the sub-block classification results. Compared with the traditional crack detection and recognition method, the method proposed by the invention has higher recognition accuracy and execution efficiency.

Description

technical field [0001] The invention relates to the fields of computer vision and pattern recognition, mainly uses machine learning methods for detection and recognition of road surface cracks, and in particular relates to a method for detection and recognition of road surface cracks based on sparse representation classification. Background technique [0002] Cracks are the most common disease on pavement, timely and accurate detection of pavement cracks is very important for the maintenance and management of high-load roads. Through artificial vision detection, a lot of manpower and material resources are required, and the detection results are subjective. The rapid development of computers has enabled people to use computers to complete automatic detection of road surface diseases. [0003] Traditional pavement crack detection is based on image processing and analysis, and then some cross-field methods have also been proposed to describe and enhance crack characteristics ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/46G06K9/62G06K9/64G06T7/00
CPCG06T7/0004G06T2207/20081G06V10/40G06V10/513G06V10/75G06F18/2136
Inventor 唐振民周舟吕建勇钱彬
Owner NANJING UNIV OF SCI & TECH
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