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Bridge surface crack detection method based on YOLO v3 and attention mechanism

A detection method and attention technology, applied in neural learning methods, image analysis, image enhancement and other directions, can solve the problems of threshold processing of noisy image data, affecting the detection accuracy of algorithms, complex image noise, etc., achieving real-time detection speed, Excellent detection effect and the effect of improving prediction accuracy

Active Publication Date: 2020-09-18
FUZHOU UNIV
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Problems solved by technology

Among the four detection algorithms, the performance of the fast Haar transform is significantly better than the other three detection algorithms, but it is difficult to perform threshold processing on image data containing noise, and the detection accuracy is much lower than that of manual detection.
Although the above algorithms have a high degree of automation, when the image noise is too complex, it will still greatly affect the detection accuracy of the algorithm.

Method used

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  • Bridge surface crack detection method based on YOLO v3 and attention mechanism
  • Bridge surface crack detection method based on YOLO v3 and attention mechanism
  • Bridge surface crack detection method based on YOLO v3 and attention mechanism

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Embodiment Construction

[0041] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0042] It should be pointed out that the following detailed description is exemplary and is intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0043] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combina...

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Abstract

The invention relates to a bridge surface crack detection method based on YOLO v3 and an attention mechanism. The method comprises the following steps: dividing a data set into a training set and a test set; a Crack-YOLO network is constructed, and an output detection box of the network is optimized; training the optimized Crack-YOLO network by adopting the training set, and testing the trained Crack-YOLO network by adopting the test set; and inputting a to-be-tested picture into the Crack-YOLO network passing the test so as to detect cracks on the surface of the bridge. The bridge crack can be accurately recognized and positioned.

Description

technical field [0001] The invention relates to the technical field of bridge surface crack detection, in particular to a bridge surface crack detection method based on YOLO v3 and an attention mechanism. Background technique [0002] Among many bridge diseases, bridge cracks are a kind of damage state that is difficult to detect, and it is also an important problem that endangers the safety of bridges. When the crack width is too large, it will directly destroy the integrity of the structure, cause concrete carbonization, peeling of the protective layer and corrosion of steel bars, greatly reduce the bearing capacity of the bridge, and even cause collapse accidents in severe cases. Therefore, taking effective measures to monitor and prevent bridge cracks plays a very important role in ensuring the safety and normal operation of bridge traffic. Among all crack detection techniques, visual inspection is the most convenient and quickest. However, manual detection is highly d...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/08G06N3/04
CPCG06T7/0002G06N3/08G06T2207/20081G06T2207/20084G06N3/045
Inventor 黄捷张岳鑫蔡逢煌齐义文王武柴琴琴蔡颖李卓敏
Owner FUZHOU UNIV
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