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Hexagonal bolt looseness detection method based on deep learning and Hough transform

A technology of deep learning and Hough transform, applied in the field of computer vision, can solve the problems of expanding safety hazards, difficulty in long-term maintenance, heavy workload, etc., and achieve the effects of improving accuracy and robustness, strong feature expression ability, and reducing costs

Active Publication Date: 2019-08-06
SOUTHEAST UNIV
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Problems solved by technology

[0002] A large number of hexagonal bolts are often used in daily life, and if the problem of hexagonal bolt loosening cannot be found immediately, it may lead to further expansion of safety hazards and cause incalculable losses
If the inspection of bolt looseness is carried out manually, there will be many points and a wide area, the workload will be heavy, and it will be difficult to maintain for a long time

Method used

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  • Hexagonal bolt looseness detection method based on deep learning and Hough transform
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  • Hexagonal bolt looseness detection method based on deep learning and Hough transform

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Embodiment Construction

[0047] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments:

[0048] The present invention provides a hexagonal bolt looseness detection method based on deep learning and Hough transform, and uses deep learning technology to realize bolt looseness detection through an R-FCN target detection algorithm. First calculate the depth feature of the input image to obtain the bolt position, and then extract the bolt image from the calculated bolt position in the original picture and calculate the bolt angle. The detection scheme designed by us can accurately detect whether the bolts are loose.

[0049] Take the hexagonal bolt of the dam reservoir door as an example, combined with the attached figure 1 The specific implementation of the deep learning-based bolt looseness detection scheme of the present invention will be further described in detail.

[0050] Step 1: Use TensorFlow1.4.0 to write the R-FCN algorithm and train...

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Abstract

The invention discloses a hexagonal bolt looseness detection method based on deep learning and Hough transform, and belongs to the technical field of computer vision. According to the method, a deep learning technology is used, and detection of bolt looseness is realized through an area-based full convolutional neural network target detection algorithm. The depth characteristics of an input picture is calculated to obtain a bolt position, a bolt image is extracted from an original picture according to the calculated bolt position, edge information is obtained through a series of image processing algorithms, and a bolt angle is calculated through Hough transform. By adopting the designed detection scheme, whether the bolt is loosened or not can be accurately detected.

Description

Technical field [0001] The invention relates to the field of computer vision technology, and in particular to a hexagonal bolt looseness detection method based on deep learning and Hough transform. Background technique [0002] A large number of hexagonal bolts are often used in daily life. If the problem of loose hexagonal bolts is not discovered immediately, it may lead to further expansion of safety hazards and cause immeasurable losses. If the bolt loosening inspection work is carried out manually, it will involve many areas and a large workload, making it difficult to maintain for a long time. A more scientific and reasonable method is: based on image processing technology, detect the bolt angle in the video captured by the surveillance camera, and determine whether the angle at each moment exceeds an unacceptable threshold when compared with the initial calibration value. It is of great significance to design such a stable and reasonable bolt looseness detection syste...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/60G06T7/11G06K9/62
CPCG06T7/0004G06T7/60G06T7/11G06T2207/10004G06T2207/20024G06T2207/20081G06T2207/20084G06T2207/30108G06F18/259G06F18/25
Inventor 杨绿溪廖如天王驭扬张旭帆邓亭强
Owner SOUTHEAST UNIV
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