CN102116659A - Interval convergence based stock bin level detection method - Google Patents
Interval convergence based stock bin level detection method Download PDFInfo
- Publication number
- CN102116659A CN102116659A CN 201010511151 CN201010511151A CN102116659A CN 102116659 A CN102116659 A CN 102116659A CN 201010511151 CN201010511151 CN 201010511151 CN 201010511151 A CN201010511151 A CN 201010511151A CN 102116659 A CN102116659 A CN 102116659A
- Authority
- CN
- China
- Prior art keywords
- image
- gear
- material level
- entropy
- width
- 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.)
- Granted
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 24
- 238000000034 method Methods 0.000 claims abstract description 39
- 238000004364 calculation method Methods 0.000 claims abstract description 11
- 239000000463 material Substances 0.000 claims description 62
- 230000011218 segmentation Effects 0.000 claims description 40
- 239000004744 fabric Substances 0.000 claims description 30
- 238000003708 edge detection Methods 0.000 claims description 4
- 238000002360 preparation method Methods 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 abstract description 9
- 238000012545 processing Methods 0.000 abstract description 4
- 238000007781 pre-processing Methods 0.000 abstract 1
- 239000003245 coal Substances 0.000 description 21
- 238000005286 illumination Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 239000000428 dust Substances 0.000 description 6
- 238000013461 design Methods 0.000 description 4
- 238000009434 installation Methods 0.000 description 4
- 238000005303 weighing Methods 0.000 description 4
- 230000007613 environmental effect Effects 0.000 description 3
- 238000004880 explosion Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 230000007812 deficiency Effects 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 239000011343 solid material Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Abstract
The invention relates to a method for uninterruptedly detecting stock bin level based on a digital camera and auxiliary lighting equipment by adopting an image processing mode. In the invention, an image is shot by a two-grading manner, and then image preprocessing is carried out according to the image characteristics, image entropy of the processed image is calculated, an approximate interval is obtained after processing and analysis are carried out a one-grading (rough grading) image, the shot image is graded secondarily (fine grading) in the interval, the image entropy of the preprocessed image is calculated, and proper images are selected to carry out image stock bin level detection, so as to obtain the stock bin level; and a stock bin level value can be obtained after calculation.
Description
Technical field
The present invention relates to the detection of bin-level.The mode that the present invention is specifically related to use the digital camera photographic images and carry out Flame Image Process is carried out the contactless detection of dynamic of bin-level.
Background technology
It is the important measures of safety in production that bin-level detects.People adopt the whole bag of tricks that bin-level is detected for many years.Detection method commonly used has: Weight type, electric pole type, condenser type, machine rod-type, Weighing type, revolution wing wheel formula, radar type, ultrasonic type, laser type, nucleon formula etc.Wherein Weight type, electric pole type, condenser type, machine rod-type, Weighing type and revolution wing wheel formula belong to contact measurement method, and remaining is a contactless measurement.The method that can carry out the extreme position measurement has: Weight type, electric pole type, nucleon formula and laser type.The method that can carry out the material level continuous coverage has: radar type, ultrasonic type, machine rod-type, Weighing type, nucleon formula, laser type etc.
The mode of employing Flame Image Process is carried out depth survey and is widely used.The employing digital camera that is mostly that adopts is at present focused automatically, and directly photographic images carries out the degree of depth, highly measures through Flame Image Process then, sees that specifically patent publication No. is the patent of invention of CN1378086.Yet, be directed to the solid material feed bin, because the image taking environment is abominable especially, there are following characteristics in the image of shooting:
(1) big, the high humidity of feed bin dust concentration, illumination is low, photographic images is of poor quality, and camera is difficult to realize automatic focus.
(2) the illumination fluctuation is frequent in the feed bin, and for example main equipment is a lot of under the coal mine, and grid disturbance is big, causes the illumination fluctuation.
(3) be directed to some special occasions, coal bunker under the coal mine for example, because requirement of explosion proof, equipment and lighting power should be as far as possible little.
Therefore, because special image-context, the method that adopts common Flame Image Process mode to carry out depth survey both had been difficult to satisfy the real-time of bin-level measurement, the requirement of reliability, also was difficult to realize long-term, stable detection.
Patent publication No. is that CN101270981 is a kind of material level measuring method and device based on machine vision, has proposed the material level detection method at the coal mine blanking bin, yet this method reliability is difficult to guarantee that practical application effect is not good.
Technical matters:
Because the image-context of feed bin is special, adopts present existing mode, has following problem:
(1) digital camera be difficult to automatic focusing, clap reliable image;
(2) it is low to carry out method fiduciary level, the precision of level gauging according to this type of image;
(3) because feed bin is a sealing, semi-enclosed environment, the monitoring of equipment operation situation and verification difficulty, neither one is based on the self-detection mechanism of the fiduciary level of detection system, and system reliability can't be guaranteed, and is difficult to practical application.
The present invention adopts the mode of using the digital camera photographic images and carrying out Flame Image Process to carry out the contactless detection of dynamic of bin-level, being directed to the feed bin environment has proposed employing calculated characteristics texture image entropy and has adopted PCNN to carry out the material level Calculation Method, image taking adopts the method for twice stepping in the detection, has the following advantages:
(1) adopts calculated characteristics texture image entropy and adopt PCNN to carry out the material level Calculation Method and reduced requirement, strengthened adaptive faculty the abominable image-context of feed bin to picture quality;
(2) detect in image taking adopt the method for twice stepping, increased real-time and reliability that material level detects.
Summary of the invention
The present invention is made up of digital camera, floor light, Flame Image Process three parts.
Digital camera:
Digital camera should be installed in the top of feed bin, and the facility of avoiding feed bin feed opening and relevant feed opening is in order to avoid be blocked shooting angle (digital camera is arranged as shown in Figure 1).Digital camera system should comprise a transparent sealed cover, the dust arrester of a seal closure and stationary installation separately.
The digital camera correlation parameter is selected according to feed bin size and floor light intensity.
Floor light:
Floor light adopts one group of shot-light, one of them contiguous digital camera setting, and all the other are the five equilibrium angle and are arranged on the coal bunker bulkhead circumference.Light source adopts monochromatic source, selects wavelength long ruddiness or infrared light supply.Physical size and environmental selection light source power and shot-light quantity (floor light is arranged as shown in Figure 2) according to coal bunker.
Floor light is primarily aimed at the feature of image design of the abominable especially feed bin of this class image-context of coal bunker under the coal mine for example, the feed bin image imaging has two main difficult points, the one, retrained by working condition, the feed bin environment exists that dust concentration is big, the characteristics of high humidity, cause the illumination decay very fast, and the floodlight deficiency; The 2nd, for security consideration, lighting power should be low as far as possible.The design of employing multi-angle illumination can effectively overcome above difficulty.
Flame Image Process comprises:
(1) image is carried out calculating respectively after the pre-service method of the image entropy of every width of cloth image:
The image entropy processing flow chart as shown in Figure 3.
The image entropy computing method:
(a) image is carried out grey level stretching
Because the purpose of Flame Image Process is to carry out material level to detect here, so can adopt gray level image.Gray level adopts 8 gray scale rank.Because the feed bin gradation of image is the skewness weighing apparatus often, so generally all will make grey level stretching in advance.
Method is as follows:
When gray scale is discrete value, the approximate probable value that replaces of frequency, that is:
p
r(r
k)＝n
k/n 0≤r
k≤1?k＝0，1，……，l-1；
In the formula: l is the total number of gray level, p
r(r
k) be the probability of getting k level gray-scale value, n
kBe the number of times that occurs k level gray scale in the image, n is a sum of all pixels in the image.
(b) image is differentiated
The note piece image be X (l, j), the image behind the differential be designated as Y (l, j).Then:
Y(1，j)＝X(1，j)；
Y(l，j)＝X(l，j)-X(i-1，j)，(i＞1)；
In image, mainly comprise two parts zone, based on the top of bulkhead with based on the bottom on material surface, wherein based on the bottom on material surface because light reflection irregular, presenting bright, dark zonule mixes, among the image Y that obtains after the calculating, based on top gray-scale value convergence 0 value of bulkhead, then obtain border bright, dark zonule based on the bottom on material surface, wherein the main body that will calculate as information entropy of this part.
(c) image being carried out two-value cuts apart
For the image behind the further clear differential, choose a threshold value and two-value is carried out on image background and the border, zonule that obtains cut apart.Obtain image Z (i, j);
(d) information entropy of calculating bianry image
Image entropy H (P): H (P)=-P
1LnP
1-P
0LnP
0
P wherein
1, P
0, represent that respectively Z is 1,0 o'clock a probability.
(2) the material level edge calculates the computing method of material level value:
For the P width of cloth image of choosing, at first,, obtain one group P material level value L with material level edge and each self-corresponding material level scale image comparison
1(i=1 ..., P), obtain actual material level value L:L=(L1+L2+...+Lp)/p by following formula then.
Material level scale image preparation method: the feed bin that with the degree of depth is h serves as to be divided into w part at interval with absolute error value Δ h, w=h/ Δ h, corresponding w gear, camera lens focal length homologue distance is since the 1st gear, order is to w gear, take according to following method: (S=1 when camera lens focal length homologue distance is S segmentation gear, 2, w), on the feed bin bulkhead of interior sky, scale is set, scale is arranged at one by one (S-t* Δ h), (S-(t-1) * Δ h), ..., (S-Δ h), S, (S+ Δ h), ..., (S+ (t-1) * Δ h), (S+t* Δ h) locates, respectively photographic images obtains one group of (2t+1) width of cloth image, this group image is carried out can obtaining after the Flame Image Process material level scale image that segments gear corresponding to S; Can obtain the whole feed bin material level scale image of w segmentation gear altogether according to the method described above, form the material level scale image sets in complete feed storehouse.
Based on interval convergent bin-level detection method, comprise following step:
(1) divide the rough segmentation shelves:
With the degree of depth is that the feed bin rough segmentation of h is N equal portions (rough segmentation shelves), according to the corresponding gear of dividing of object distance, adopts program controlled mode to regulate digital camera lens focus photographic images, obtains corresponding one group of N width of cloth image dividing gear;
(2) determine the interval of convergence:
Image is carried out calculating respectively after the pre-service image entropy of every width of cloth image, and (1＜K＜N) remembers that minimum gear is Ni in the gear of this K width of cloth image correspondence, and maximum gear is Nj, has then determined an interval of convergence [Ni, Nj] to select the bigger K width of cloth image of entropy.
(3) divide the segmentation shelves:
Interval [Ni, Nj] is subdivided into M equal portions (segmentation shelves),, adopts program controlled mode to regulate digital camera lens focus photographic images, obtain corresponding one group of M width of cloth image dividing gear according to the corresponding gear of dividing of object distance;
(4) edge detects and calculates the material level value:
Image is carried out calculating respectively after the pre-service image entropy of every width of cloth image, choose the bigger P width of cloth image of entropy, adopt material level edge detection algorithm to calculate the material level edge, and calculate the material level value.
The choosing method of rough segmentation shelves N, segmentation shelves M:
The absolute error value that the segmentation gear detects according to bin-level requires to be provided with, and the gear spacing of promptly getting the segmentation shelves equals absolute error value; The full scale actual range that the rough segmentation shelves detect according to bin-level reaches for the rate request setting that detects, and rough segmentation shelves gear spacing is generally the integral multiple of segmentation shelves gear spacing.
The frontier point Ni of the interval of convergence [Ni, Nj], the value of Nj:
By the definite interval of convergence [Ni, Nj] of image that the rough segmentation shelves are taken, the ratio of the image entropy of Ni and the pairing image of Nj (with the bigger entropy of less entropy) should be greater than the ratio of regulation.
Fail-safe analysis:
Fail-safe analysis 1:
According to the characteristics that image entropy distributes, in the image that the rough segmentation shelves are taken, determined an interval of convergence [Ni, Nj]; Wherein during value, the ratio of the image entropy of Ni and the pairing image of Nj (with the bigger entropy of less entropy) should be greater than the ratio k 1 of regulation.K1 is according to feed bin image-context value, and is relevant with environmental parameters such as the material variety of feed bin, feed bin illumination, humidity, dust concentrations.K1 generally should be greater than 0.8.If can not satisfy this condition, illustrate in this shooting process that system works occurs unusual, data are unavailable.Need photographic images again, if problem does not still solve, may there be hardware fault in illustrative system, needs maintenance.
Fail-safe analysis 1 program circuit as shown in Figure 4.
Fail-safe analysis 2:
The image that meter segments the shelves shooting is B1, and B2......B10 chooses B1, and the big P width of cloth image of entropy carries out the material level detection among the B2......B10, obtain the material level value and (be designated as M (i), i=1,2, ..., P) between error should be less than the least error Y* of project demand, and be positioned at its corresponding gear interval.
Fail-safe analysis 2 program circuits as shown in Figure 5.
The system works flow process:
At first, the feed bin height is designated as h, and institute's material level that requires detects absolute error value and is designated as d, and stepping is divided into segmentation shelves and rough segmentation shelves dual mode.With the full scale rough segmentation of bin-level is N equal portions (rough segmentation shelves), according to the corresponding gear of dividing of object distance, adopt program controlled mode to regulate digital camera lens focus photographic images, obtain corresponding one group of N width of cloth image dividing gear, image is carried out calculating respectively after the pre-service image entropy of every width of cloth image, for the image that adopts this method to take, camera focus is the closer to the material level place, and the image of shooting is clear more, and image entropy is big more, the K width of cloth image that the selection entropy is bigger (1＜K＜N), remember that gear minimum in the gear of this K width of cloth image correspondence is Ni, maximum gear is Nj, has determined a interval of convergence [Ni, Nj], then this interval should comprise actual material level value; With interval [Ni, Nj] be subdivided into M equal portions (segmentation shelves), according to the corresponding gear of dividing of object distance, adopt program controlled mode to regulate digital camera lens focus photographic images, obtain corresponding one group of M width of cloth image dividing gear, image is carried out calculating respectively after the pre-service image entropy of every width of cloth image, then the entropy of this set of diagrams picture should be more approaching, because the material level of feed bin often is not the plane, the changing features that presents behind each blanking is very big, therefore cause the entropy of image can not accurately reflect material level, thus can not be simply with the position of the image of entropy maximum as near the gear of material level.At this moment choose the bigger P width of cloth image of entropy, adopt the method for PCNN, calculate the material level edge,, obtain one group P material level value L then according to prefabricated image scale background image
1(i=1 ..., P), obtain actual material level value L by following formula then:
Proceeding next round then detects.
Bin-level measuring system workflow diagram as shown in Figure 6.
Description of drawings
Fig. 1 digital camera arrangenent diagram
Fig. 2 floor light arrangenent diagram
Fig. 3 image entropy processing flow chart
Fig. 4 fail-safe analysis 1 program circuit
Fig. 5 fail-safe analysis 2 program circuits
Fig. 6 bin-level measuring system workflow diagram
Embodiment
The present invention further specifies as follows in conjunction with the embodiments referring to accompanying drawing:
With coal bunker under the coal mine is example, coal bunker height 40m, and diameter 8m, the detection absolute error value is 0.5m.The spacing value of minimum feed bin is 4m.
Set segmentation gear 1m, the rough segmentation shelves are 4m.Digital camera selects for use program to regulate lens focus, and floor light adopts 8 shot-lights.
Before detecting, prefabricated material level scale background image.
The feed bin that with the degree of depth is 40m serves as to be divided into w part at interval with absolute error value Δ h=0.5m, w=h/ Δ h=80, corresponding w gear, camera lens focal length homologue distance is since the 1st gear, order is to w gear, take according to following method: (S=1 when camera lens focal length homologue distance is S segmentation gear, 2, w), on the feed bin bulkhead of interior sky, scale is set, scale is arranged at one by one (S-t* Δ h), (S-(t-1) * Δ h), ..., (S-Δ h), S, (S+ Δ h), ..., (S+ (t-1) * Δ h), (S+t* Δ h) locates (t=4 here), respectively photographic images obtains one group of (2t+1=9) width of cloth image, this group image is carried out can obtaining after the Flame Image Process material level scale image that segments gear corresponding to S; Can obtain the whole feed bin material level scale image of w segmentation gear altogether according to the method described above, form the material level scale image sets in complete feed storehouse.
Choose k1=0.8, absolute error value=0.25m.
Choose k=3, P=4.
Referring to accompanying drawing 1, the installation site of digital camera system has been described, digital camera system should be installed in the top of feed bin, and as far as possible near bin wall, the facility of avoiding feed bin feed opening and relevant feed opening is in order to avoid be blocked shooting angle.Digital camera system should comprise a transparent sealed cover, the dust arrester of a seal closure and stationary installation separately, device also should satisfy the safety requirements of application scenario, uses when of the present invention as coal bunker under in coal mine, and device used in the present invention also should satisfy electrical explosion proof requirement under the coal mine.
Referring to accompanying drawing 2, the installation of floor light has been described, auxiliary lighting system adopts one group of shot-light, one of them contiguous digital camera setting, and all the other are the five equilibrium angle and are arranged on the coal bunker bulkhead circumference.Light source adopts monochromatic source, selects wavelength long ruddiness or infrared light supply.Physical size and environmental selection light source power and shot-light quantity according to coal bunker.Accompanying drawing 2 has been described the situation of 8 shot-lights.Use when of the present invention as coal bunker under in coal mine, device used in the present invention also should satisfy electrical explosion proof requirement under the coal mine.
Auxiliary lighting system is primarily aimed at the feature of image design of the abominable especially feed bin of this class image-context of coal bunker under the coal mine for example, the feed bin image imaging has two main difficult points, the one, retrained by working condition, the feed bin environment exists that dust concentration is big, the characteristics of high humidity, cause the illumination decay very fast, and the floodlight deficiency; The 2nd, for security consideration, lighting power should be low as far as possible.The design of employing multi-angle illumination can effectively overcome above difficulty.
Referring to accompanying drawing 3, the characteristic image image entropy computing method of detection method of the present invention have been described, at first image is carried out the pre-service computing, may further comprise the steps: grey level stretching, differential calculation, two-value are cut apart, and then calculate its image entropy.
Material level testing process figure as shown in Figure 6.
When beginning to detect, at first, 101, can obtain one group of 10 width of cloth image, order carries out 102,103, judge 104 then, that remember three entropy minimums is Z1, and that maximum is Z2, k '=Z1/Z2, then when k '＞k1, then checking is passed through, illustrate that the image of taking is reliable, system works is normal, zero clearing this moment proof mark position, carry out next step flow process, when k '＜=k1, illustrate that the image of taking is unreliable, system works is undesired, this moment, the proof mark position should be zero, at first with position, proof mark position, judged 105 then, when zone bit X1 is 1, re-execute 101,102, after 103 processes, enter 104 once more, if the unusual of this subsystem is that accidental interference causes, then should recover normal this moment, k '＞k1 then, checking can be passed through, if the functional fault of system occurred, then checking can not be passed through, when judging the proof mark position, zone bit is 1, then carries out fault alarm.
After 104 checkings are passed through, carry out 106, in between the material level detection zone that stepping is determined according to segmentation photographic images grade one by one, can obtain one group of 9 width of cloth image, order carries out 107,108, adopt the method for PCNN to carry out the edge detection of material level, the material level scale image comparison with the respective notch place obtains the material level value then.Judge 109 at last, the individual material level value of the P that analysis obtains (P=4), calculated difference, error should be less than desired accuracy value, then continue checking material level value if the verification passes and whether be in corresponding gear place, by then the key diagram picture is reliable, system works is normal, next step flow process is carried out in zero clearing this moment proof mark position.More than two judge that any one by set zone bit X1 then, judges 105 then, when zone bit X1 is 1, re-execute 101,102,103,104,106,107,108,109, if the unusual of this subsystem is that accidental interference causes, then should recover normal this moment, checking can be passed through, if the functional fault of system occurred, then checking can not be passed through, when judging the proof mark position, zone bit is 1, then carries out fault alarm.After by 109, carry out 110, the output bin-level value of averaging is proceeded next round then and is detected.
Claims (7)
1. one kind based on interval convergent bin-level detection method, it is characterized in that: with the degree of depth is that the feed bin rough segmentation of h is N equal portions (rough segmentation shelves), according to the corresponding gear of dividing of object distance, adopt program controlled mode to regulate digital camera lens focus photographic images, obtain corresponding one group of N width of cloth image dividing gear, image is carried out calculating respectively after the pre-service image entropy of every width of cloth image, the K width of cloth image that the selection entropy is bigger (1＜K＜N), remember that gear minimum in the gear of this K width of cloth image correspondence is Ni, maximum gear is Nj, then determined an interval of convergence [Ni, Nj]; With interval [Ni, Nj] be subdivided into M equal portions (segmentation shelves), according to the corresponding gear of dividing of object distance, adopt program controlled mode to regulate digital camera lens focus photographic images, obtain corresponding one group of M width of cloth image dividing gear, image is carried out calculating respectively after the pre-service image entropy of every width of cloth image, choose the bigger P width of cloth image of entropy, adopt material level edge detection algorithm to calculate the material level edge, and calculate the material level value.
2. detection method as claimed in claim 1, earlier image is carried out pre-service, again to the computing method of pretreated image calculation image entropy, it is characterized in that: at first image is carried out the pre-service computing, may further comprise the steps: grey level stretching, differential calculation, two-value are cut apart, and then calculate its image entropy.
3. detection method as claimed in claim 1, material level edge detection algorithm is characterized in that: the segmentation shelves are taken the bin-level image that obtains, after the image pre-service, carry out the PCNN edge and detect, obtain the regional edge edge, be the material level edge.
4. detection method as claimed in claim 1 calculates the computing method of material level value according to the material level edge, it is characterized in that: for the P width of cloth image of choosing, at first, with material level edge and each self-corresponding material level scale image comparison, obtain one group P material level value L
1(i=1 ..., P), obtain actual material level value L:L=(L1+L2+...+Lp)/p by following formula then.
5. detection method as claimed in claim 4, material level scale image preparation method, it is characterized in that: the feed bin that with the degree of depth is h serves as to be divided into w part at interval with absolute error value Δ h, w=h/ Δ h, corresponding w gear, camera lens focal length homologue distance is since the 1st gear, order is to w gear, take according to following method: (S=1 when camera lens focal length homologue distance is S segmentation gear, 2,, w), on the feed bin bulkhead of interior sky, scale is set, scale is arranged at one by one (S-t* Δ h), (S-(t-1) * Δ h), ..., (S-Δ h), S, (S+ Δ h), ..., (S+ (t-1) * Δ h), (S+t* Δ h) locates, respectively photographic images obtains one group of (2t+1) width of cloth image, this group image is carried out can obtaining after the Flame Image Process material level scale image that segments gear corresponding to S; Can obtain the whole feed bin material level scale image of w segmentation gear altogether according to the method described above, form the material level scale image sets in complete feed storehouse.
6. detection method as claimed in claim 1, the division methods of rough segmentation shelves and segmentation shelves is characterized in that: the absolute error value Δ h that the segmentation gear detects according to bin-level requires to be provided with, and the gear spacing of promptly getting the segmentation shelves equals absolute error value Δ h; The rough segmentation shelves are according to feed bin degree of depth h and the rate request setting to detecting, and rough segmentation shelves gear spacing is the integral multiple of segmentation shelves gear spacing.
7. detection method as claimed in claim 1, the interval of convergence [Ni, Nj] frontier point Ni, the value of Nj, it is characterized in that: by the definite interval of convergence [Ni of image of rough segmentation shelves shooting, Nj], the ratio of the image entropy of Ni and the pairing image of Nj (with the bigger entropy of less entropy) should be greater than the ratio of regulation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201010511151 CN102116659B (en) | 2010-10-19 | 2010-10-19 | Interval convergence based stock bin level detection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201010511151 CN102116659B (en) | 2010-10-19 | 2010-10-19 | Interval convergence based stock bin level detection method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102116659A true CN102116659A (en) | 2011-07-06 |
| CN102116659B CN102116659B (en) | 2012-12-26 |
Family
ID=44215529
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201010511151 Active CN102116659B (en) | 2010-10-19 | 2010-10-19 | Interval convergence based stock bin level detection method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102116659B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105389795A (en) * | 2015-10-13 | 2016-03-09 | 中联重科股份有限公司 | Garbage level detection method, garbage level detector and feeding board machine |
| CN109870211A (en) * | 2019-01-22 | 2019-06-11 | 珠海格力电器股份有限公司 | Store up detection method, device and the rice bucket of rice amount in rice equipment |
| CN112772508A (en) * | 2021-01-18 | 2021-05-11 | 上海览宋科技有限公司 | Batch feeder |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1994018549A1 (en) * | 1993-02-03 | 1994-08-18 | Mefos, Stiftelsen För Metallurgisk Forskning | A method for simultaneously measuring the positions of more than one surface in metallurgic processes |
| JPH08178732A (en) * | 1994-12-27 | 1996-07-12 | Nittetsu Mining Co Ltd | Method and apparatus for detecting level using laser light |
| JPH10148561A (en) * | 1996-11-15 | 1998-06-02 | Natl Fedelation Of Agricult Coop Assoc | Method and device for measuring level of bulk material |
| US6098029A (en) * | 1994-06-14 | 2000-08-01 | Hitachi, Ltd. | Liquid-level position measuring method and system |
| JP2000298054A (en) * | 1999-04-15 | 2000-10-24 | Niigata Eng Co Ltd | Method and device for measuring liquid level |
| CN1286395A (en) * | 2000-09-29 | 2001-03-07 | 上海交通大学 | Imaging measurement method for volume of large material stack |
| CN201600164U (en) * | 2009-12-29 | 2010-10-06 | 新疆信通水利电子有限公司 | Video water-level gauge |
-
2010
- 2010-10-19 CN CN 201010511151 patent/CN102116659B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1994018549A1 (en) * | 1993-02-03 | 1994-08-18 | Mefos, Stiftelsen För Metallurgisk Forskning | A method for simultaneously measuring the positions of more than one surface in metallurgic processes |
| US6098029A (en) * | 1994-06-14 | 2000-08-01 | Hitachi, Ltd. | Liquid-level position measuring method and system |
| JPH08178732A (en) * | 1994-12-27 | 1996-07-12 | Nittetsu Mining Co Ltd | Method and apparatus for detecting level using laser light |
| JPH10148561A (en) * | 1996-11-15 | 1998-06-02 | Natl Fedelation Of Agricult Coop Assoc | Method and device for measuring level of bulk material |
| JP2000298054A (en) * | 1999-04-15 | 2000-10-24 | Niigata Eng Co Ltd | Method and device for measuring liquid level |
| CN1286395A (en) * | 2000-09-29 | 2001-03-07 | 上海交通大学 | Imaging measurement method for volume of large material stack |
| CN201600164U (en) * | 2009-12-29 | 2010-10-06 | 新疆信通水利电子有限公司 | Video water-level gauge |
Non-Patent Citations (1)
| Title |
|---|
| 《计算机应用与软件》 20031231 王蔚等 基于图像清晰度的自动聚焦算法 第2.1节至第3.2节 1-4、6-7 , 第12期 2 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105389795A (en) * | 2015-10-13 | 2016-03-09 | 中联重科股份有限公司 | Garbage level detection method, garbage level detector and feeding board machine |
| CN105389795B (en) * | 2015-10-13 | 2018-09-28 | 长沙中联重科环境产业有限公司 | A kind of rubbish material level detection method, device and feeding trigger |
| CN109870211A (en) * | 2019-01-22 | 2019-06-11 | 珠海格力电器股份有限公司 | Store up detection method, device and the rice bucket of rice amount in rice equipment |
| CN112772508A (en) * | 2021-01-18 | 2021-05-11 | 上海览宋科技有限公司 | Batch feeder |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102116659B (en) | 2012-12-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102116661B (en) | Method for detecting dynamic stock level in stock bin limit position based on machine vision | |
| CN107506798B (en) | Water level monitoring method based on image recognition | |
| De Vries et al. | Remote sensing of surf zone waves using stereo imaging | |
| Gangeskar | An algorithm for estimation of wave height from shadowing in X-band radar sea surface images | |
| CN102564508B (en) | Method for implementing online tests of stream flow based on video images | |
| CN109460753A (en) | A method of detection over-water floats | |
| CN107588823A (en) | Water gauge water level measurement method based on dual-waveband imaging | |
| US20150213299A1 (en) | Infrared image based early detection of oil spills in water | |
| CN101957309A (en) | All-weather video measurement method for visibility | |
| KR102169452B1 (en) | METHOD FOR ENSURING STABILITY OF DATA COLLECTED IN IoT WEATHER ENVIRONMENT | |
| CN102116659B (en) | Interval convergence based stock bin level detection method | |
| CN101813523B (en) | Method for measuring atmospheric coherence length of mobile beacon | |
| CN109764930A (en) | A kind of water gauge water level line visible detection method suitable for complex illumination condition | |
| CN104569923B (en) | Velocity restraint-based Hough transformation fast track starting method | |
| CN109886205A (en) | Safety belt method of real-time and system | |
| CN104182992B (en) | Method for detecting small targets on the sea on the basis of panoramic vision | |
| CN111046761B (en) | Belt load distribution detection system and detection method based on multi-sensor information fusion | |
| CN114450711A (en) | Workpiece surface defect detection device and detection method, workpiece surface inspection system, and program | |
| JP2021056182A (en) | Apparatus and method for detecting surface defect of workpiece, surface inspection system for workpiece, and program | |
| CN107271445A (en) | A kind of defect inspection method and device | |
| CN110132246A (en) | RS Fathoming detection method based on residual error subregion | |
| CN105607218A (en) | Image auto-focusing method measurement data transmission device and method based on fuzzy entropy | |
| CN103729644A (en) | Satellite tracking method, overcoming interference caused when fixed star passes through probe window, of foundation optical measuring equipment | |
| CN106960437B (en) | Liquid level detection method for oil-immersed power transformer of power inspection robot | |
| CN116047112A (en) | Method, device and storage medium for measuring surface flow velocity of fluid |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |