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Subway tunnel disease detection method based on deep learning

A tunnel disease and deep learning technology, applied in the field of image processing and deep learning, can solve problems such as time-consuming, inefficient, and slow manual inspection, and achieve the effect of improving representativeness and good results

Pending Publication Date: 2021-11-19
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In view of the shortcomings of slow, time-consuming and inefficient manual inspection in subway tunnel inspection, machine learning algorithms can be used for visual inspection to solve the problem of low maintenance efficiency of workers. The traditional machine learning algorithm SVM is currently used for visual inspection, but its Efficiency and accuracy still have a lot to be improved

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  • Subway tunnel disease detection method based on deep learning
  • Subway tunnel disease detection method based on deep learning
  • Subway tunnel disease detection method based on deep learning

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Embodiment Construction

[0037] The present invention is described in further detail now in conjunction with accompanying drawing.

[0038] It should be noted that terms such as "upper", "lower", "left", "right", "front", and "rear" quoted in the invention are only for clarity of description, not for Limiting the practicable scope of the present invention, and the change or adjustment of the relative relationship shall also be regarded as the practicable scope of the present invention without substantive changes in the technical content.

[0039] The present invention recognizes and learns a large amount of subway tunnel surface image information through the deep neural network technology, realizes accurate positioning and classification of different tunnel diseases, and then performs disease detection and evaluation on the identified tunnel surface areas. figure 1 The general workflow of the subway tunnel defect detection method based on deep learning is shown.

[0040] Step S10 , creating an image ...

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Abstract

The invention discloses a subway tunnel disease detection method based on deep learning, and mainly solves the problems that the subway tunnel detection amount is large, and the existing detection algorithm is low in efficiency and large in missing detection amount. The method specifically comprises the following steps: making a to-be-detected subway tunnel image database, including a subway tunnel normal region picture database and a subway tunnel disease region database; based on an improved Faster R-CNN target detection algorithm, carrying out target positioning and accurate identification and classification on the subway tunnel diseases; detecting a disease state in the to-be-detected area by adopting a deep learning-based method; and finally, inputting a to-be-tested image into the detection model to obtain a tunnel disease detection result. The solution thought of the traditional algorithm is image-preprocessing-artificial feature extraction-classification. According to the method, the problems existing in current subway tunnel disease detection are solved by utilizing the thought of deep learning, namely image-feature extraction network-classification and regression.

Description

technical field [0001] The invention relates to the fields of image processing and deep learning, in particular to a method for detecting subway tunnel defects based on deep learning. Background technique [0002] The vigorous development and expansion of urban rail transit has further promoted the rise of China's urban rail operation and maintenance market. The existing tunnel safety maintenance has low labor efficiency, high operation intensity, short window time, high maintenance cost, poor effectiveness, and overall planning. Problems such as weak capabilities and insufficient connection with operations have put forward higher requirements for tunnel safety detection methods. With the continuous development of deep learning technology, breakthroughs have been made in many fields, such as AlphaGo defeating the international Go champion, real-time "machine translation" in the call video, recognizing information in pictures or videos like humans, etc. . In view of the sho...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62
CPCG06T7/0004G06T2207/20081G06T2207/20084G06T2207/30132G06F18/231G06F18/24G06F18/214
Inventor 梁宏宇汪俊吕松阳梁以恒姜策王宇涵陈泽玙奥利弗·戴维斯
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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