Sparse-representation-classification-based pavement crack detection and identification method

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: 2016-04-06
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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  • Sparse-representation-classification-based pavement crack detection and identification method
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  • Sparse-representation-classification-based pavement crack detection and identification method

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Embodiment Construction

[0024] 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.

[0025] 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.

[0026] According to the embodiment of the present invention, the pavement crack detection and recognition method based on sp...

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Abstract

The invention provides a sparse-representation-classification-based pavement crack detection and identification method. A sparse representation-based classifier (SRC) is introduced and an effective sub-block high-order moment characteristic is selected, thereby avoiding pretreatment and post treatment of an image, simplifying detection steps, and improving the operation efficiency. The method comprises: carrying out extraction and normalization of training set (subblock) characteristic vectors; dividing a tested image into a plurality of sub blocks and extracting features of the sub blocks, and carrying out classification by using an SRC; and identifying a crack type according to a mapping code of a sub block classification result. Compared with the traditional crack detection and identification method, the provided method has the higher identification precision and higher 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 ...

Claims

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Application Information

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Patent Type & Authority Applications(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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