Pavement crack rapid identification method based on deep learning

A technology of pavement cracks and identification methods, applied in the field of image processing, can solve the problems of less research on crack morphology and feature maintenance activities, etc.

Active Publication Date: 2020-05-08
BEIJING UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0007] 3. There are many studies on crack automatic identification and crack classification, but there are few studies on crack morphology characteristics caused by different reasons and different maintenance activities

Method used

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  • Pavement crack rapid identification method based on deep learning
  • Pavement crack rapid identification method based on deep learning
  • Pavement crack rapid identification method based on deep learning

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Embodiment Construction

[0064] The invention adopts the ZOYON-RTM intelligent road detection vehicle to collect on-site images of asphalt pavement cracks. The detection system is equipped with advanced on-board sensor system, on-board computer and embedded integrated multi-sensor synchronous control unit.

[0065] The pavement damage detection system is equipped with a linear array camera with a resolution of 2048 pixels / line and an infrared laser road auxiliary lighting system to ensure all-weather detection of pavement cracks. When the test vehicle is driving at a speed of 5-100 km / h during the day, the line scan camera at the rear of the vehicle body can continuously capture images of the road surface at high speed. At the same time, infrared filters are used to remove shadows caused by sunlight. These high-quality images have sufficient resolution to ensure that pavement cracks can be directly identified by the human eye.

[0066] Although the pavement inspection system was designed using noise...

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Abstract

The invention discloses a pavement crack rapid identification method based on deep learning, and the method comprises the steps: firstly adjusting the size of a field picture, and then adjusting the exposure of the field picture, wherein original on-site photos are uneven in illumination, and the shielding effect of on-site images can be achieved only by processing uneven exposure; fitting the distribution of grayscale pixel values by using a histogram of pixel intensity values based on grayscale distribution; achieving binary color visualization by using a threshold method based on the mean value of the previous step; if the pixel value is greater than the threshold value, setting the pixel value as a background; enhancing the fracture shape by using a connecting member-based method; using a connection tool to perform denoising; searching all connected objects in the graph, and checking the area of the crack shape; if the area of the fracture shape is less than a threshold, considerting that the object is noise; if the area of the crack shape is greater than the threshold, considerting that the object is a crack; finally, adjusting CNN input, and reconnecting cracks through expansion and erosion; and adjusting CNN hyper-parameters, and determining an optimal CNN frame.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a method for quickly identifying pavement cracks based on deep learning. The invention is applied to unprocessed original road surface crack scene pictures and identifies four types of cracks. Background technique [0002] Cracking is one of the inevitable problems in pavement hazards. For the detection of cracks, civil engineers currently mostly detect them through manual visual inspection or indoor experiments. But these methods are cumbersome and labor-intensive, slowing down the efficiency of pavement maintenance. [0003] In this regard, there have been many image processing methods for quickly detecting pavement cracks and identifying cracks, such as directional edge detection methods using noise to analyze pavement images; structural state monitoring methods based on adaptive visual crack detection; using convolutional neural networks (Crack Net) an automatic detection met...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/34G06N3/04G06N3/08
CPCG06N3/08G06V10/267G06N3/045G06F18/24Y02A30/60
Inventor 侯越彭勃王俊涛杨湛宁陈逸涵曹丹丹
Owner BEIJING UNIV OF TECH
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