Road crack identification method based on deep learning

A technology of crack identification and deep learning, applied in the field of crack identification, can solve the problems of high cost, impact on traffic, manpower consumption, etc., achieve fast detection speed, avoid blindness, and reduce casualties

Inactive Publication Date: 2019-10-11
CHINA JILIANG UNIV
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Problems solved by technology

Traditional detection methods based on artificial vision are increasingly unable to meet the requirements of expressway d

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  • Road crack identification method based on deep learning
  • Road crack identification method based on deep learning
  • Road crack identification method based on deep learning

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Embodiment Construction

[0038] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0039] like figure 1 As shown, a road crack identification method based on deep learning, the specific steps are as follows:

[0040] Step 1, the road crack image is obtained from the road site photos and the network, including different environments, lighting, road surfaces, and shapes, and is divided into a training data set and a test data set at a ratio of 9:1. Some sample examples are as follows: figure 2 (a)- figure 2 As shown in (d), the training data set is used to train the Faster R-CNN model, and the test data set is used to verify the quality of the Faster R-CNN model. Use the LabelImg tool to mark the crack information in the road crack image. Create a road crack image dataset according to the PascalVOC dataset format used by Faster R-CNN. Mark the cracks in the image and generate an XML file for subsequent Faster R-...

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Abstract

The invention discloses a road crack identification method based on deep learning. The method comprises the following steps that firstly, road crack images are collected, a training set is established, and a convolutional neural network is constructed to extract features in the images to generate a feature map; faster R-CNN model is trained, the model comprises an RPN network, an RoI Pooling network and a full connection layer which are connected in sequence, the RPN network obtains a detection target and an image background and obtains a candidate box position; and then candidate regions aregenerated, the RoI Pooling network outputs a RoI feature map with a fixed size, the feature map generated by the convolutional neural network and the RoI feature map are integrated, the object category of the detection target is discriminated, and the accurate position of the object is returned. Finally, a to-be-identified road image is input into the trained Faster R-CNN model, and whether the image is a road crack image or not is judged. The method has the advantages of high detection speed and high recognition accuracy.

Description

technical field [0001] The invention relates to the technical field of crack identification, in particular to a method for identifying road cracks based on deep learning. Background technique [0002] In recent decades, expressways have developed vigorously in China, and the subsequent post-construction maintenance has increasingly become a problem. It is necessary to regularly check the condition of expressway pavement in order to formulate corresponding maintenance strategies. One of the important items is Indicators are road cracks. If the crack can be found in the early stage of appearance and its development can be tracked in time, then its maintenance cost will be greatly reduced. How to conduct real-time detection on the entire road surface without affecting normal traffic conditions has become a major problem to be solved urgently. Traditional detection methods based on artificial vision are increasingly unable to meet the requirements of expressway development. It...

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Application Information

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IPC IPC(8): G06K9/00G06K9/32G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/00G06V10/25G06N3/045G06F18/241
Inventor 范昕炜李太文
Owner CHINA JILIANG UNIV
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