CN104331710A - On-off state recognition system - Google Patents
On-off state recognition system Download PDFInfo
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- CN104331710A CN104331710A CN201410668236.2A CN201410668236A CN104331710A CN 104331710 A CN104331710 A CN 104331710A CN 201410668236 A CN201410668236 A CN 201410668236A CN 104331710 A CN104331710 A CN 104331710A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
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Abstract
The invention relates to the technical field of remote digital video monitoring and image recognition, in particular to an on-off state recognition system which comprises an image collection module, a communication module, an image preprocessing module, an image feature processing and recognition module and a template matching module. Euclidean distance is adopted as a classifier. Euclidean distance has the highest recognition rate so it can effectively classify the features of on-off states; Euclidean distance is applicable to a twisted type on-off state classification algorithm, has the shortest average classification time, and accordingly can meet the timeliness requirement of the algorithm. The on-off state recognition system automatically obtains training sample bases from all obtained original sample bases through a training sample base building module, enables the quantity and size of the whole training sample sets to be appropriate, and can effectively recognize the on-off state without influencing the system speed.
Description
Technical field
The present invention relates to remote digital video monitoring and image identification technical field, be specifically related to a kind of on off state recognition system.
Background technology
In power scheduling process, Main Basis isolating switch assists node to judge state and the position of switch.But, due to burn into wearing and tearing, the reason such as aging, cause auxiliary switch to switch not in place, sometimes correctly can not judge the actual position of switch, for command scheduling provides error message.Now need manually to go the observation that scene is carried out on the spot, and discharge fault and generally need the longer time, bring unsafe factor to mains supply, even need interruption maintenance.
Remote digital video monitoring and image recognition technology combine by remote digital video monitoring and image identification system exactly, first by equipment such as camera, demoder and video servers, the digital video signal collected is passed back Surveillance center in real time in the mode of video flowing by transmission channel, in Surveillance center, remote video monitoring is carried out to scene, intercept from video flowing and monitor Target Photo, digital video image is analyzed by corresponding Preprocessing Technique, process and identifies.Therefore apply monitoring remote video and image recognition technology, the automatic identification to the state of power high voltage circuit breaker switch and fault warning can be realized.This technology is ensure that electric power enterprise production safety and quick diagnosis fault provide a kind of intuitively and accurately means newly.
But traditional switch is mostly by colour recognition or pass through Text region, such as, to the identification etc. of the font of display " ON " or " OFF ", for the identification of some redirect switches etc., then cannot identify accurately, cause the operation strategies of system comparatively to limit to.
Summary of the invention
Solve the problems of the technologies described above, the invention provides a kind of on off state recognition system, be applicable to the state recognition of redirect switch, recognition effect is good, and accuracy is high.
In order to achieve the above object, the technical solution adopted in the present invention is, a kind of on off state recognition system, comprise image capture module, communication module, image pre-processing module, characteristics of image processing and identification module and template matches module, described image capture module and communication module are arranged on motor machine room, described communication module is connected with image capture module telecommunications, described image pre-processing module, characteristics of image identification module and template matches module are all arranged on Surveillance center, and image pre-processing module carries out data interaction by communication module and image capture module
Described image capture module to switch disk rotary on off state Real-time Collection transition cashier's office in a shop,
The realtime image data that image capture module collects by described communication module is transferred to image pre-processing module,
The realtime image data that described image pre-processing module received communication module transmits, and obtain pretreatment image after image gray processing, image smoothing, image sharpening and image binaryzation process are carried out to this view data,
Described characteristics of image identification module is connected with image pre-processing module, extracts pretreatment image and determines current disk rotary on off state feature transition,
Described template matches module measured switch status flag mates with the on off state image in given Sample Storehouse, calculates similarity measure values, obtain the state conclusion of current disc rotary switch by similarity measurement.
Further, described template matches module adopts Euclidean distance computing method, calculates switch samples X to be identified and ω
isample in class Sample Storehouse
distance d, wherein
be ω
iq sample in class Sample Storehouse, its computing formula is as follows:
The distance of switch samples X more to be identified again and each sample of all class Sample Storehouse kinds, and carry out following judgement:
If meet the switch samples X more to be identified of 2. formula, be then judged to be X ∈ ω
i, switch samples X state namely to be identified is ω
ithe on off state of class Sample Storehouse, otherwise be judged to be
Further, also comprise training sample database in described template matches module and set up module, this module is by design original sample storehouse, redundant samples storehouse and training sample database, to each sample training in original sample storehouse, and training result is classified, and put into redundant samples storehouse or training sample database respectively, thus automatically obtain training sample database.This mode makes the population size of training sample set moderate, effectively can distinguish the classification of traffic sign, be unlikely to again the speed of the system that has influence on.
Further, described image pre-processing module comprises and is converted to gray-scale map unit and image denoising unit.The color switching image collected is converted into gray level image by the described gray-scale map unit that is converted into, and described image denoising unit carries out medium filtering to gray level image, is removed by the noise point existed, avoid the interference that noise spot brings image recognition in gray scale.
Further, described characteristics of image identification module comprises image smoothing unit, image sharpening unit, image binaryzation unit and feature identification unit, described image smoothing unit makes image display effect more clean to the smoothing process of image, it is more obvious that switch and background distinguish effect, gray-scale map is carried out binaryzation by described image binaryzation unit, switch is separated further with background, and feature identification unit is used for judging on off state.
The present invention is by adopting technique scheme, and compared with prior art, tool has the following advantages:
The present invention chooses Euclidean distance as sorter, and the discrimination of Euclidean distance is the highest, illustrates that it can effectively be classified redirect switch shape facility, compares and be applicable to redirect switch Shape Classification, and its average classification shortest time, meet algorithm requirement of real-time.Native system of the present invention is from all original sample storehouses obtained, set up module by training sample database and automatically obtain training sample database, make the population size of whole training sample set moderate, effectively can distinguish the classification of switch shape, be unlikely to again the speed of the system that has influence on.The present invention is directed to redirect switch and not only can reach Real time identification, discrimination is higher simultaneously.
Accompanying drawing explanation
Fig. 1 is the structural representation of embodiments of the invention;
Fig. 2 is the image pre-processing module of embodiments of the invention, the functional status schematic diagram of characteristics of image processing and identification module and template matches module;
Fig. 3 (a) is the former figure of on off state video of the intercepting of embodiments of the invention;
Fig. 3 (b) is the image after the on off state gray processing of embodiments of the invention;
Fig. 3 (c) is the image after the on off state image smoothing of embodiments of the invention;
Fig. 3 (d) is the image after the on off state image sharpening of embodiments of the invention;
Fig. 3 (e) is the image after the on off state image binaryzation of embodiments of the invention;
Fig. 3 (f) is the image of the switch sections that the on off state feature identification of embodiments of the invention is irised out.
Fig. 3 (g) is the measured switch binary image that the on off state feature of embodiments of the invention intercepts out.
Fig. 4 is that the training sample database of embodiments of the invention generates block diagram.
Fig. 5 is the on off state OPEN constitutional diagram of embodiments of the invention.
Fig. 6 is the on off state CLOSE constitutional diagram of embodiments of the invention.
Fig. 7 is the constitutional diagram of other states of on off state of embodiments of the invention.
Embodiment
Now the present invention is further described with embodiment by reference to the accompanying drawings.
As a specific embodiment, as depicted in figs. 1 and 2, a kind of on off state recognition system of the present invention, comprise image capture module, communication module, image pre-processing module, characteristics of image processing and identification module and template matches module, described image capture module and communication module are arranged on motor machine room, described communication module is connected with image capture module telecommunications, described image pre-processing module, characteristics of image identification module and template matches module are all arranged on Surveillance center, and image pre-processing module carries out data interaction by communication module and image capture module,
Described image capture module to switch disk rotary on off state Real-time Collection transition cashier's office in a shop, shown in figure 3 (a), the former figure of video of the intercepting of the on off state intercepted for the present embodiment.
The realtime image data that image capture module collects by described communication module is transferred to image pre-processing module,
The realtime image data that described image pre-processing module received communication module transmits, and obtain pretreatment image after image gray processing, image smoothing, image sharpening and image binaryzation process are carried out to this view data, with reference to figure 3 (b), Fig. 3 (c), Fig. 3 (d), Fig. 3 (e) are respectively the image that image gray processing, image smoothing, image sharpening and image binaryzation process obtain.
Described characteristics of image identification module is connected with image pre-processing module, extract pretreatment image and determine current disk rotary on off state feature transition, shown in figure 3 (f) He Fig. 3 (g), for determining the image of current disk rotary on off state feature transition and carrying out the on off state figure after image binaryzation process to this image.
Described template matches module measured switch status flag mates with the on off state image in given Sample Storehouse, calculates similarity measure values, obtain the state conclusion of current disc rotary switch by similarity measurement.
Described template matches module adopts Euclidean distance computing method, and known on off state has OPEN and CLOSE two classifications, can be set to: ω
1and ω
2.Every class has N
iindividual sample image, then ω
iclass is expressed as
for switch samples to be identified, X=(x
1, x
2... x
n), calculate the distance of itself and OPEN, CLOSE state sample.Calculate switch samples X to be identified and ω
isample in class Sample Storehouse
distance d, wherein
be ω
iq sample in class Sample Storehouse, its computing formula is as follows:
The distance of switch samples X more to be identified again and each sample of all class Sample Storehouse kinds, and carry out following judgement:
If meet the switch samples X more to be identified of 2. formula, be then judged to be X ∈ ω
i, switch samples X state namely to be identified is ω
ithe on off state of class Sample Storehouse, otherwise be judged to be
Also comprise training sample database in described template matches module and set up module, this module is by design original sample storehouse, redundant samples storehouse and training sample database, to each sample training in original sample storehouse, and training result is classified, and put into redundant samples storehouse or training sample database respectively.This division, makes the population size of training sample set moderate, effectively can distinguish the state of redirect switch, be unlikely to again the speed of the system that has influence on.
For obtaining switch image in multi-light, angle, position as sample set, carry out a large amount of experiments, under outdoor fine day, cloudy day, the different illumination condition such as cloudy, fixing camera, change the state, position, distance etc. of switch, the switch samples image that shooting etc. are large, makes on off state identification storehouse, for the research of the detection and indentification of on off state.The recognizer major part used on off state detection and indentification system is all the method for Corpus--based Method study, and discrimination is very strong to the dependence of training set.Sample for training is more, and pattern more (illumination, weather, light, angle, position etc.), the generalization ability of sorter is stronger, and the recognition effect obtained is better.But training sample too much can cause the room and time complexity of system can be very high.
For this reason, shown in figure 4, native system, from all original sample storehouses obtained, is set up module by training sample database and is automatically obtained training sample database, comprise the following steps:
1. detected by switch binary image and obtain original sample storehouse O, and original sample storehouse is divided into OPEN, two class O of CLOSE two states
1, O
2;
2. the original state of training sample and redundant samples is empty;
3. each class O in pair original sample storehouse
i, carry out the extraction of training sample respectively, get a certain class sample O in original sample storehouse
iin the 1st
put in training sample database, and delete in original sample storehouse
4. all sample training in pair training sample database, obtain proper vector storehouse;
5. compare all samples in original sample storehouse and proper vector storehouse compares, obtain the minor increment D (j) (j=1,2) in each sample and proper vector storehouse, wherein, N is O
iin current total sample number.
6. find the maximum D (j) in 4, the sample namely corresponding to max (D (j))
see whether it meets threshold condition max (D (j))>=Th
min, wherein Th
minfor sample
the minimum threshold of training sample database can be entered, if meet threshold condition, then by sample
put into Sample Storehouse, otherwise, then do not put into;
7. find out all samples that minor increment is less than threshold condition, namely meet D (j) <Th
max, wherein, Th
maxfor sample
the max-thresholds in sample redundancy storehouse can be entered, put into redundancy storehouse;
8. repeat the process of step 4 ~ 6, until original sample storehouse O
ifor sky;
9. repeat the process of step 2 ~ 8, until O
i(i=1,2) all obtain training sample database.
With manually select compared with storehouse, automatically select the advantage of storehouse system to be: (1) overcomes the randomness manually selecting storehouse, can a large amount of manpowers and time be saved; (2) automatically select storehouse system can select the sample of maximum inter-object distance, make sample more accurate, more representative, redundancy is little, decreases the quantity of Sample Storehouse.
Described image pre-processing module comprises and is converted to gray-scale map unit and image denoising unit.The color switching image collected is converted into gray level image by the described gray-scale map unit that is converted into, and described image denoising unit carries out medium filtering to gray level image, is removed by the noise point existed, avoid the interference that noise spot brings image recognition in gray scale.
Described characteristics of image identification module comprises image smoothing unit, image sharpening unit, image binaryzation unit and feature identification unit, described image smoothing unit makes image display effect more clean to the smoothing process of image, it is more obvious that switch and background distinguish effect, gray-scale map is carried out binaryzation by described image binaryzation unit, switch is separated further with background, and feature identification unit is used for judging on off state.
Finally, through above-mentioned judgement, the switch obtained has come to three kinds of states: OPEN state, CLOSE state and other states (as: switch is covered by other article, and camera goes wrong).
When system judges that measured switch image is the state of OPEN.The lamp of the OPEN on interface will be shown as green, and the lamp of CLOSE is shown as yellow.As shown in Figure 5, OPEN state.
When system judges that measured switch image is the state of CLOSE.The lamp of the CLOSE on interface will be shown as green, and the lamp of OPEN is shown as yellow.As shown in Figure 6, CLOSE state.
When system judges that measured switch image is other states, two lamps will show redness simultaneously, namely occur mistake.As shown in Figure 7, other states are judged to be.
Although specifically show in conjunction with preferred embodiment and describe the present invention; but those skilled in the art should be understood that; not departing from the spirit and scope of the present invention that appended claims limits; can make a variety of changes the present invention in the form and details, be protection scope of the present invention.
Claims (5)
1. an on off state recognition system, it is characterized in that: comprise image capture module, communication module, image pre-processing module, characteristics of image processing and identification module and template matches module, described image capture module and communication module are arranged on motor machine room, described communication module is connected with image capture module telecommunications, described image pre-processing module, characteristics of image identification module and template matches module are all arranged on Surveillance center, and image pre-processing module carries out data interaction by communication module and image capture module
Described image capture module to switch disk rotary on off state Real-time Collection transition cashier's office in a shop,
The realtime image data that image capture module collects by described communication module is transferred to image pre-processing module,
The realtime image data that described image pre-processing module received communication module transmits, and obtain pretreatment image after image gray processing, image smoothing, image sharpening and image binaryzation process are carried out to this view data,
Described characteristics of image identification module is connected with image pre-processing module, extracts pretreatment image and determines current disk rotary on off state feature transition,
Described template matches module measured switch status flag mates with the on off state image in given Sample Storehouse, calculates similarity measure values, obtain the state conclusion of current disc rotary switch by similarity measurement.
2. a kind of on off state recognition system according to claim 1, is characterized in that: described template matches module adopts Euclidean distance computing method, calculates switch samples X to be identified and ω
isample in class Sample Storehouse
distance d, wherein
be ω
iq sample in class Sample Storehouse, its computing formula is as follows:
The distance of switch samples X more to be identified again and each sample of all class Sample Storehouse kinds, and carry out following judgement:
If meet the switch samples X more to be identified of 2. formula, be then judged to be X ∈ ω
i, switch samples X state namely to be identified is ω
ithe on off state of class Sample Storehouse, otherwise be judged to be
3. a kind of on off state recognition system according to claim 1, it is characterized in that: also comprise training sample database in described template matches module and set up module, this module is by design original sample storehouse, redundant samples storehouse and training sample database, to each sample training in original sample storehouse, and training result is classified, and put into redundant samples storehouse or training sample database respectively, thus automatically obtain training sample database.This mode makes the population size of training sample set moderate, can effectively distinguish redirect on off state, be unlikely to again the speed having influence on system.
4. a kind of on off state recognition system according to claim 1, is characterized in that: described image pre-processing module comprises and is converted to gray-scale map unit and image denoising unit.The color switching image collected is converted into gray level image by the described gray-scale map unit that is converted into, and described image denoising unit carries out medium filtering to gray level image, is removed by the noise point existed, avoid the interference that noise spot brings image recognition in gray scale.
5. a kind of on off state recognition system according to claim 1, it is characterized in that: described characteristics of image identification module comprises image smoothing unit, image sharpening unit, image binaryzation unit and feature identification unit, described image smoothing unit makes image display effect more clean to the smoothing process of image, it is more obvious that switch and background distinguish effect, gray-scale map is carried out binaryzation by described image binaryzation unit, and switch is separated further with background.
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| CN106250902A (en) * | 2016-07-29 | 2016-12-21 | 武汉大学 | Power system on off state detection method based on characteristics of image template matching |
| CN106339722A (en) * | 2016-08-25 | 2017-01-18 | 国网浙江省电力公司杭州供电公司 | Line knife switch state monitoring method and device |
| CN106570865A (en) * | 2016-11-08 | 2017-04-19 | 国家电网公司 | Digital-image-processing-based switch state detecting system of power equipment |
| CN108068817A (en) * | 2017-12-06 | 2018-05-25 | 张家港天筑基业仪器设备有限公司 | A kind of automatic lane change device and method of pilotless automobile |
| CN108334815A (en) * | 2018-01-11 | 2018-07-27 | 深圳供电局有限公司 | Method for inspecting, on off state recognition methods and the system of second power equipment |
| CN108334824A (en) * | 2018-01-19 | 2018-07-27 | 国网电力科学研究院武汉南瑞有限责任公司 | High voltage isolator state identification method based on background difference and iterative search |
| CN109100760A (en) * | 2018-08-16 | 2018-12-28 | 集美大学 | A kind of Beidou and satellite communication bimodulus high accuracy positioning Internet of Things terminal |
| CN109409395A (en) * | 2018-07-29 | 2019-03-01 | 国网上海市电力公司 | Using the method for template matching method identification target object region electrical symbol in power monitoring |
| CN111382673A (en) * | 2020-01-09 | 2020-07-07 | 南京艾拓维讯信息技术有限公司 | KVM system and method for monitoring windmill power generation state |
| CN112178706A (en) * | 2020-10-14 | 2021-01-05 | 宁波方太厨具有限公司 | Method and system for identifying fire gear of stove and method and system for linking smoke stove |
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106250902A (en) * | 2016-07-29 | 2016-12-21 | 武汉大学 | Power system on off state detection method based on characteristics of image template matching |
| CN106339722A (en) * | 2016-08-25 | 2017-01-18 | 国网浙江省电力公司杭州供电公司 | Line knife switch state monitoring method and device |
| CN106570865A (en) * | 2016-11-08 | 2017-04-19 | 国家电网公司 | Digital-image-processing-based switch state detecting system of power equipment |
| CN108068817A (en) * | 2017-12-06 | 2018-05-25 | 张家港天筑基业仪器设备有限公司 | A kind of automatic lane change device and method of pilotless automobile |
| CN108334815A (en) * | 2018-01-11 | 2018-07-27 | 深圳供电局有限公司 | Method for inspecting, on off state recognition methods and the system of second power equipment |
| CN108334824A (en) * | 2018-01-19 | 2018-07-27 | 国网电力科学研究院武汉南瑞有限责任公司 | High voltage isolator state identification method based on background difference and iterative search |
| CN109409395A (en) * | 2018-07-29 | 2019-03-01 | 国网上海市电力公司 | Using the method for template matching method identification target object region electrical symbol in power monitoring |
| CN109100760A (en) * | 2018-08-16 | 2018-12-28 | 集美大学 | A kind of Beidou and satellite communication bimodulus high accuracy positioning Internet of Things terminal |
| CN111382673A (en) * | 2020-01-09 | 2020-07-07 | 南京艾拓维讯信息技术有限公司 | KVM system and method for monitoring windmill power generation state |
| CN112178706A (en) * | 2020-10-14 | 2021-01-05 | 宁波方太厨具有限公司 | Method and system for identifying fire gear of stove and method and system for linking smoke stove |
| CN112178706B (en) * | 2020-10-14 | 2021-11-05 | 宁波方太厨具有限公司 | Method and system for identifying fire gear of stove and method and system for linking smoke stove |
| CN113901964A (en) * | 2021-12-07 | 2022-01-07 | 北京惠朗时代科技有限公司 | Staff face excitement detection method and system for intelligent company management |
| CN114202731A (en) * | 2022-02-15 | 2022-03-18 | 南京天创电子技术有限公司 | Multi-state knob switch identification method |
| CN116907349A (en) * | 2023-09-12 | 2023-10-20 | 北京宝隆泓瑞科技有限公司 | Universal switch state identification method based on image processing |
| CN116907349B (en) * | 2023-09-12 | 2023-12-08 | 北京宝隆泓瑞科技有限公司 | Universal switch state identification method based on image processing |
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