CN105321173B - A kind of train crossing cable cleat automatic defect detection method based on machine vision - Google Patents
A kind of train crossing cable cleat automatic defect detection method based on machine vision Download PDFInfo
- Publication number
- CN105321173B CN105321173B CN201510611588.9A CN201510611588A CN105321173B CN 105321173 B CN105321173 B CN 105321173B CN 201510611588 A CN201510611588 A CN 201510611588A CN 105321173 B CN105321173 B CN 105321173B
- Authority
- CN
- China
- Prior art keywords
- image
- fixture
- pipeline
- carried out
- threshold
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
Landscapes
- Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
A kind of train crossing cable cleat automatic defect detection method based on machine vision of the disclosure of the invention, is related to the processing of cable cleat image in image processing field, particularly train crossing.Fixture image is gathered first;Vertical projection is carried out to image, and judges whether there is pipeline in picture according to drop shadow curve, pipeline location is determined if having pipeline, and intercept pipeline image;Statistics with histogram is carried out, is carried out negating binaryzation according to Low threshold, then judges whether there is fixture on the pipeline in piping drawing picture, if there is fixture to determine chucking position;Statistics with histogram is carried out to interception image, is carried out negating binaryzation according to high threshold, judges whether the fixture on the pipeline in piping drawing picture comes off.With it is easy, quick, efficiently find the fixture that comes off, to change in time.
Description
Technical field
The present invention relates to the processing of cable cleat image in image processing field, particularly train crossing.
Background technology
Cable cleat is the elimination vortex phenomenon in order to prevent from producing movement because external force suffix is conducted oneself with dignity after cable laying, and
Prevent cable due to electrodynamic generation and bounce and the offset phenomena of generation, thus it is solid to need to use fixture to be segmented cable
It is fixed.Because train crossing cable cleat arrangement environment is complicated, the card long-time aging on cable cleat comes off, it is necessary to coming off
Fixture carry out replacing processing.Train mileage in China's is long, it is necessary to which the tunnel road of detection is more and complicated at present, and China uses at present
Or artificial visual the fixture that comes off of method detection, not only Detection accuracy is low, speed is slow, cost is high, and in railway
On detected and dangerous by people, such as how most lower cost, accuracy rate detection quick, higher faster have come off fixture
The fixture of upper clamp slice is into a major issue in maintaining cables.
The content of the invention
In order to overcome the test problems that inner cable fixture upper clamp slice in tunnel comes off on rail link, the invention provides based on
The simplicity of machine vision, fast and efficiently detection method, so as to find the fixture to come off rapidly, to change in time.
The technical scheme is that analyzed by the shape special efficacy of the dash area to fixture so as to confirm fixture
It whether there is and judge whether fixture is intact, therefore a kind of train crossing cable cleat automatic defect detection based on machine vision
Method, comprise the following steps that:
Step 1:High speed acquisition video camera is loaded ON TRAINS, tunnel cable clamp is gathered during railway operation
Greyscale video, and position and the velocity information of collection point are recorded simultaneously;
Step 2:Video is resolved into single image, between-line spacing selection is entered to image, then located as follows to choosing image
Reason;
Step 3:Vertical projection is carried out to image, and judges whether there is pipeline in image according to drop shadow curve, if there is pipeline
Pipeline location is then determined, and intercepts pipeline image;
Step 4:Statistics with histogram is carried out to the image that step 3 obtains, is carried out negating binaryzation according to Low threshold, then judge
Whether there is fixture on pipeline in piping drawing picture, if there is fixture to determine chucking position;
Step 5:Statistics with histogram is carried out to the image that step 3 obtains, is carried out negating binaryzation according to high threshold, judges to manage
Whether the fixture on pipeline in road image comes off.
Further, the image obtained in the step 4 to step 3 carries out negating binaryzation according to Low threshold, due to folder
Tool and pipeline threshold value are variant, can leave a small amount of connected region after Low threshold processing, judge piping drawing picture according to connected region
In pipeline on whether have fixture.
Further, the image obtained in the step 5 to step 3 carries out negating binaryzation according to high threshold, by two-value
Change the maximum transversal distance of connected region in image compared with the width of pipeline, if being shorter than duct width, judge fixture
Intermediate plate come off.
The determination method of Low threshold and high threshold is in the step 4,5:
Step 3 is obtained into image and carries out statistics with histogram, histogram is divided into by left and right or upper and lower two according to fixture direction
Part, two-part crest value being recorded respectively, relatively low crest value being set as Low threshold, higher crest value is set as height
Threshold value.
The beneficial effect of the system is, it is only necessary to image capturing system can is installed on normal train to train usually
Detected along wire holder, it is simple efficient.By the image procossing to collection, this method can rapidly detect Along Railway tunnel
Cable clamp surface fixture card is opened and come off in road.
Brief description of the drawings
Fig. 1 is system flow chart
Fig. 2 is the OK sample images that embodiment 1 gathers
Fig. 3 is the drop shadow curve of embodiment 1
Fig. 4 is region of interesting extraction image
Fig. 5 is Low threshold segmentation figure
Fig. 6 is high threshold segmentation figure
Fig. 7 is the fixture image navigated to
Fig. 8 is the NG sample images that embodiment 2 gathers
Fig. 9 is the region of interest area image extracted
Figure 10 is Low threshold segmentation figure picture
Figure 11 is high threshold segmentation figure picture
Figure 12 is the fixture image that embodiment 2 navigates to
1. pipeline in figure, 2. fixtures, 3. fixture cards
Embodiment
Fig. 1 is system algorithm implementing procedure figure, including the algorithm stream of video image acquisition and ensuing image procossing
Journey.
The fixture example images of Fig. 2 preferably, wherein have pipeline 1, fixture 2, fixture card 3.
Video image (being gathered by previous step 1) is gathered by CCD camera first, then the video image collected is divided
Then solution carries out X-axis projection, drop shadow curve is as shown in Figure 3 into single-frame images, and by frame input algorithm to image.(due to this reality
When camera is placed in example, in order that the image of cable in the picture is as long as possible, so camera is laterally disposed, i.e., on image
Vertical direction is horizontal direction in practice, and the horizontal direction in image is vertical direction in practice).
Next pipeline location is determined according to image projection curve, as shown in Fig. 3 figures, huge depression is electricity among curve
Cable pipeline position;Then region of interesting extraction is carried out to image, i.e., cable is extracted according to cable channel position
Image, the image extracted are as shown in Figure 4.
And then the segmentation of Low threshold image is carried out to the image extracted and high threshold image is split to obtain two segmentation figures
As (this two segmentation figure pictures have all carried out inversion operation, i.e., when threshold value is x, the pixel less than x is white, and what it is higher than x is
Black), the step is the core of this algorithm, determines whether there is fixture on pipeline using the segmentation figure picture of Low threshold,
If fixture can then determine the position of fixture by this segmentation image;Using high threshold image judge fixture whether UNICOM.
This example is that Low threshold will using the fixed threshold (this threshold value can be changed) set manually, the key of threshold value setting
The pedestal of fixture can be embodied, high threshold wants the shade of energy display pipes upper clip, is found by surveying when polishing condition is identical,
It is stable and reliable using fixed threshold.
Next area filtering is carried out to the image of Low threshold segmentation, wherein Low threshold image is split and filters out small area
For image as shown in figure 5, equally being operated to high threshold segmentation figure picture, high threshold segmentation is as shown in Figure 6.
And then the connected domain coordinate setting fixture coordinate obtained according to Low threshold image segmentation (Fig. 5), and in high threshold
Interception holder part segmentation figure picture in segmentation figure picture (Fig. 6), as shown in Figure 7.Finally in the figure 7 according to connected domain widthwise size come
Judge whether the intermediate plate on fixture comes off.(set if there is the lateral length of connected domain more than given duct length by oneself
It is fixture (in embodiment as shown in Figure 8) that is intact, otherwise being come off for fixture card then to judge its fixture calmly), and exports knot
Fruit, that is, the picture position for the fixture card that comes off.
When the image to be come off for fixture card of input, example image is as shown in figure 8, Fig. 8 is the NG figures detected
Picture.Wherein there was only pipeline 1, fixture 2.Wherein Figure 10 is that Low threshold image is split and filters out the image after small area, and Figure 11 is height
Threshold Image Segmentation.Then the UNICOM domain coordinate setting chucking position obtained after being split according to Low threshold, and in high threshold image
Middle to intercept the image navigated to, image is as shown in figure 12 after interception.
The mileage information and the result of fixture detection output recorded during according to collection image, being calculated needs to change fixture
Fixture particular location.
Claims (2)
1. a kind of train crossing cable cleat automatic defect detection method based on machine vision, is comprised the following steps that:
Step 1:High speed acquisition video camera is loaded to the gray scale for gathering tunnel cable fixture during railway operation ON TRAINS
Video, and position and the velocity information of collection point are recorded simultaneously;
Step 2:Video is resolved into single image, between-line spacing selection is entered to image, then be handled as follows to choosing image;
Step 3:Vertical projection is carried out to image, and judges whether there is pipeline in image according to drop shadow curve, it is true if having pipeline
Determine pipeline location, and intercept pipeline image;
Step 4:Statistics with histogram is carried out to the image that step 3 obtains, is carried out negating binaryzation according to Low threshold, then judge pipeline
Whether there is fixture on pipeline in image, if there is fixture to determine chucking position;
Step 5:Statistics with histogram is carried out to the image that step 3 obtains, is carried out negating binaryzation according to high threshold, judges piping drawing
Whether the fixture on pipeline as in comes off;Determination methods are:By the maximum transversal of connected region in obtained binary image
Distance is compared with the width of pipeline, if being shorter than duct width, judges that the intermediate plate of fixture comes off;
The determination method of Low threshold and high threshold is in the step 4,5:
Step 3 is obtained into image and carries out statistics with histogram, histogram is divided into by left and right or upper and lower two parts according to fixture direction,
Two-part crest value is recorded respectively, relatively low crest value is set as Low threshold, and higher crest value is set as high threshold.
2. a kind of train crossing cable cleat automatic defect detection method based on machine vision as claimed in claim 1, its
The image for being characterised by obtaining step 3 in the step 4 carries out negating binaryzation according to Low threshold, due to fixture and pipeline threshold
Be worth it is variant, Low threshold processing after can leave a small amount of connected region, judged according to connected region on the pipeline in piping drawing picture
Whether fixture is had.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510611588.9A CN105321173B (en) | 2015-09-23 | 2015-09-23 | A kind of train crossing cable cleat automatic defect detection method based on machine vision |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510611588.9A CN105321173B (en) | 2015-09-23 | 2015-09-23 | A kind of train crossing cable cleat automatic defect detection method based on machine vision |
Publications (2)
Publication Number | Publication Date |
---|---|
CN105321173A CN105321173A (en) | 2016-02-10 |
CN105321173B true CN105321173B (en) | 2017-11-17 |
Family
ID=55248488
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201510611588.9A Active CN105321173B (en) | 2015-09-23 | 2015-09-23 | A kind of train crossing cable cleat automatic defect detection method based on machine vision |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN105321173B (en) |
Families Citing this family (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107169951B (en) * | 2016-03-03 | 2019-08-30 | 成都交大光芒科技股份有限公司 | A kind of the missing detection method and system of the inclined cantilever end pipe cap based on image |
CN111208146B (en) * | 2018-11-22 | 2023-05-16 | 北京世纪东方智汇科技股份有限公司 | Tunnel cable detection system and detection method |
CN111207304B (en) * | 2018-11-22 | 2021-09-14 | 北京世纪东方通讯设备有限公司 | Railway tunnel leaky cable vision inspection device and product positioning detection method |
CN110428398A (en) * | 2019-07-04 | 2019-11-08 | 华中科技大学 | A kind of high iron catenary bracing wire defect inspection method based on deep learning |
CN116500048B (en) * | 2023-06-28 | 2023-09-15 | 四川联畅信通科技有限公司 | Cable clamp defect detection method, device, equipment and medium |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101694694A (en) * | 2009-10-22 | 2010-04-14 | 上海交通大学 | Finger identification method used in interactive demonstration system |
CN102122351A (en) * | 2011-03-01 | 2011-07-13 | 哈尔滨工程大学 | Intelligent identification method for pipeline defect on basis of RBF (Radical Basis Function) neural network |
CN103324930A (en) * | 2013-06-28 | 2013-09-25 | 浙江大学苏州工业技术研究院 | License plate character segmentation method based on grey level histogram binaryzation |
CN104346858A (en) * | 2014-10-10 | 2015-02-11 | 新达通科技股份有限公司 | Paper currency face value identification method and device based on magnetic image |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9042647B2 (en) * | 2013-06-06 | 2015-05-26 | Xerox Corporation | Adaptive character segmentation method and system for automated license plate recognition |
-
2015
- 2015-09-23 CN CN201510611588.9A patent/CN105321173B/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101694694A (en) * | 2009-10-22 | 2010-04-14 | 上海交通大学 | Finger identification method used in interactive demonstration system |
CN102122351A (en) * | 2011-03-01 | 2011-07-13 | 哈尔滨工程大学 | Intelligent identification method for pipeline defect on basis of RBF (Radical Basis Function) neural network |
CN103324930A (en) * | 2013-06-28 | 2013-09-25 | 浙江大学苏州工业技术研究院 | License plate character segmentation method based on grey level histogram binaryzation |
CN104346858A (en) * | 2014-10-10 | 2015-02-11 | 新达通科技股份有限公司 | Paper currency face value identification method and device based on magnetic image |
Non-Patent Citations (1)
Title |
---|
一种新的挖掘眼部结构特征的人眼精定位方法;张静 等;《计算机工程与应用》;20150325;第158-162页 * |
Also Published As
Publication number | Publication date |
---|---|
CN105321173A (en) | 2016-02-10 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN105321173B (en) | A kind of train crossing cable cleat automatic defect detection method based on machine vision | |
CN102395037B (en) | Format recognition method and device | |
CN104392224B (en) | A kind of highway pavement crack detecting method | |
CN102930287B (en) | A kind of detection number system and method for overlooking pedestrian | |
CN104318582B (en) | Detection method for bad state of rotating double-lug component pin of high-speed rail contact network | |
CN103955948B (en) | A kind of space movement target detection method under dynamic environment | |
CN102980896B (en) | High ferro overhead contact line device auricle fracture detection method | |
CN104504377B (en) | A kind of passenger on public transport degree of crowding identifying system and method | |
EP2775423A3 (en) | Object detection apparatus, vehicle-mounted device control system and carrier medium of program of object detection | |
CN102288614B (en) | Method for detecting pantograph crack fault based on curvelet domain moving parallel window | |
CN108982512A (en) | A kind of circuit board detecting system and method based on machine vision | |
CN106469312A (en) | Weld seam visual identity method based on region growing label | |
CN102307274A (en) | Motion detection method based on edge detection and frame difference | |
EP2523165A3 (en) | Image processing method and image processing device | |
CN106340019A (en) | Method for detecting adverse state of high-speed rail overhead line system inclined cable fixing hook | |
CN204789357U (en) | Electrolytic capacitor outward appearance packing defect image detection system | |
CN103021179B (en) | Based on the Safe belt detection method in real-time monitor video | |
CN109711256A (en) | A kind of low latitude complex background unmanned plane target detection method | |
CN105718964A (en) | Transmission line vibration damper visual detection method | |
CN111561967A (en) | Real-time online detection method and system for pantograph-catenary operation state | |
CN105913008A (en) | Crowd exceptional event detection method based on hypothesis examination | |
CN112288674A (en) | Vehicle-mounted contact network component defect online identification method | |
CN104598906B (en) | Vehicle outline detection method and its device | |
CN113159046B (en) | Ballastless track bed foreign matter detection method and device | |
CN106446934A (en) | High speed rail overhead contact system carrier cable seat support pedestal detection method based on HOG (Histogram) features |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |