CN106156771A - A kind of meter reading Region detection algorithms based on multi-feature fusion - Google Patents

A kind of meter reading Region detection algorithms based on multi-feature fusion Download PDF

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CN106156771A
CN106156771A CN201610513983.8A CN201610513983A CN106156771A CN 106156771 A CN106156771 A CN 106156771A CN 201610513983 A CN201610513983 A CN 201610513983A CN 106156771 A CN106156771 A CN 106156771A
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water meter
characteristic
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CN106156771B (en
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金连文
刘孝睿
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South China University of Technology SCUT
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    • G06V20/60—Type of objects
    • G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
    • G06V20/63—Scene text, e.g. street names
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Abstract

One disclosed by the invention meter reading based on multi-feature fusion Region detection algorithms, comprises the following steps: S1, acquisition training data；S2, cut out reading area and non-reading area in water meter image, extract the multi-channel feature of this cutting zone and carry out Feature Fusion, with this feature for input training objective grader；S3, extraction water meter image multi-channel feature, calculate its characteristic-integration figure；S4, utilize characteristic-integration figure calculate each sliding window feature, with fusion feature for input, utilize S2 training gained grader sliding window is classified, obtain target window；S5, with extrapolation method estimate artwork characteristic pattern under multiple yardsticks, repeat S4, S5, obtain multiple dimensioned target window；S6, rotation artwork, repeat S3, S4, S5, S6, obtain multidirectional target window.The invention provides a kind of accurate, robust, practical meter reading Region detection algorithms.

Description

A kind of meter reading Region detection algorithms based on multi-feature fusion
Technical field
The present invention relates to artificial intelligence field, calculate particularly to a kind of meter reading region detection based on multi-feature fusion Method.
Background technology
In recent years, along with the development of mobile Internet and popularizing of digital product, come from distinct device (intelligence hands The photographic head such as machine, digital camera, even automatic Pilot streetscape car, unmanned plane) view data continue explosion type ground increase.These In the image of magnanimity, there is quite a few view data to carry Word message, and Word message generally contains and is highly profitable Semantic information.Such as, these Word messages are probably the description to building, shop, traffic sign, guideboard, trade name etc.. Therefore, the semantic information of these high levels can be widely applied to machine reading, automatic bat is translated, image retrieval, video frequency searching, language The occasions such as speech translation, automatic Pilot, robot navigation.The mankind need the vision character analysis skill of a kind of intelligence more urgently Art.Vision character analysis is a kind of technology extracting and understanding Word message from the angle of machine vision.It relates at image A series of subject knowledges such as reason, pattern recognition, computer vision, machine learning and psychology, are the most all association areas One of important research direction.
Water meter automatic reading based on computer vision is exactly an important application in vision character analysis, and it can take For existing artificial meter reading mode so that meter reading becomes automatic flow.The text analyzing of view-based access control model primarily solves Problem certainly is exactly the detection of character area.
The problem that the water meter automatic reading of view-based access control model primarily solves is exactly the detection of reading area, the method for current main flow It is method based on image procossing, is examined by image denoising, image binaryzation based on color characteristic, direction based on line detection The steps such as survey, region segmentation determine reading area.But this method is to the illumination under various complex scenes, deformation, coverage Bad etc. condition adaptability, easily it is disturbed, poor robustness.
Summary of the invention
It is an object of the invention to the shortcoming overcoming prior art with not enough, it is provided that a kind of water meter based on multi-feature fusion Reading area detection algorithm.
The purpose of the present invention is realized by following technical scheme:
A kind of meter reading Region detection algorithms based on multi-feature fusion, comprises the steps of
S1, acquisition training data, shoot water meter image pattern by photographic head, and enter the meter reading region in image Pedestrian's work marks, and obtains the center of meter reading, length and width information；
Reading area and non-reading area in S2, cutting water meter image, the multi-channel feature extracting this cutting zone is gone forward side by side Row Feature Fusion, with extract multi-channel feature for input training image grader；Described cutting zone is uncertain region, Classified by Image Classifier, be divided into reading area and non-reading area；
S3, extract water meter image multi-channel feature, described multi-channel feature include gradient orientation histogram, gradient magnitude, LUV color characteristic, greyscale color feature, calculate the characteristic-integration figure of water meter image；
S4, traveling through whole sliding window, utilize characteristic-integration figure to calculate each sliding window feature, the image utilizing S2 train is classified Sliding window is classified by device, obtains target window；
S5, with extrapolation method estimate artwork characteristic-integration figure under multiple yardsticks, repeat S4, S5 step, obtain many chis The target window of degree；
S6, rotation artwork, repeat S3, S4, S5, S6 step, obtain multidirectional target window.
Described step S1 particularly as follows:
S1.1, by the water meter image pattern in RGB camera collection actual scene；
S1.2, the meter reading region in water meter image pattern acquired in S1.1 is carried out artificial mark, including The center in meter reading region (x, y), length h, width w and angle a.
Described water meter image pattern includes following different parameter: illumination, visual angle, water meter type, the water meter extent of damage.This Sample is to ensure that the multiformity of sample.
Described step S2 particularly as follows:
S2.1, mark according to step S1 gained water meter image and reading area, cut out reading area and non-reading, be used for The classification of target；
S2.2, the multi-channel feature of extraction S2.1 institute cutting image, with multi-channel feature for input, train integrated decision tree Whether grader is to classify in reading area to this region.
Described step S3 particularly as follows:
S3.1, extract water meter image multi-channel feature, including gradient orientation histogram, gradient magnitude, LUV color characteristic, Greyscale color feature, and calculate characteristic-integration figure；
The computational methods of described gradient direction:
Wherein (i j) is pixel (i, j) angle of place's gradient direction to O；I is image, and x represents horizontal direction, and y represents and hangs down Nogata to, i represents pixel coordinate in the horizontal direction；J represents pixel coordinate in vertical direction；
The computational methods of described gradient magnitude:
Wherein (i j) is the gradient magnitude at pixel (i.j) place to M；I is image, and x represents horizontal direction, and y represents Vertical Square To, i represents pixel coordinate in the horizontal direction；J represents pixel coordinate in vertical direction.
Described step S4 particularly as follows:
S4.1, travel through each sliding window, utilize step S3 gained characteristic-integration figure, calculate the multi-channel feature of each sliding window, And carry out Feature Fusion；
S4.2, with S4.1 merge characteristic vector for input, utilize S2 training gained Image Classifier sliding window is carried out Classification, obtains sliding window significance；
S4.3, the target detected is carried out maximization suppression, obtain detect target.
Described step S5 particularly as follows:
S5.1, utilize the statistical property of adjacent scalogram picture, by the method for extrapolation estimate multiple dimensioned under artwork feature, Extrapolation algorithm is as follows:
C S ≈ R ( C , s ) · s - λ Ω ,
The wherein yardstick of S representative image, CsRepresenting the characteristics of image under yardstick S, (C is s) that characteristic pattern C is carried out yardstick to R Resampling for S；λ is the channel parameters that the statistical analysis by great amount of images obtains；Ω represents different image channels；Extrapolation Algorithm quickly calculates the characteristics of image of multiple adjacent yardstick by the characteristics of image calculated under a certain fixed size；
S5.2, repeat S4, S5, obtain multiple dimensioned under reading area target window.
Described step S6 particularly as follows:
With 5 degree for interval, multiple rotary original water gauge image, repeats S3, S4, S5, S6 step, is calculated multidirectional water Meter reading region, i.e. obtains multidirectional target window.
The present invention compared with prior art, has the advantage that and beneficial effect:
1, the present invention utilizes the Multi resolution feature extraction method of multiple features fusion, enriching under the multiple yardsticks of efficient extraction Information, solves the primary difficult problem in water meter automatic reading, meter reading region detection the most multiple dimensioned, multidirectional, has weight Big application prospect.
2, the present invention uses the thinking of target detection, utilizes Multi resolution feature extraction method, the multiple yardsticks of efficient extraction Under abundant information, thus under multi-angle, multiple dimensioned image, carry out sliding window scanning rapidly, obtain confidence level maximum Reading area target.It addition, the method that present invention employs multiple features fusion, therefore algorithm robustness is strong, to various complicated fields The adaptability of scape is good.The present invention solves the reading area test problems of the multi-direction water meter under complex scene, for further Recognition of Reading provides the foundation.
Accompanying drawing explanation
Fig. 1 is the flow chart of a kind of meter reading Region detection algorithms based on multi-feature fusion of the present invention.
Fig. 2 is the training flow chart of algorithm described in Fig. 1.
Detailed description of the invention
Below in conjunction with embodiment and accompanying drawing, the present invention is described in further detail, but embodiments of the present invention do not limit In this.
As it is shown in figure 1, meter reading Region detection algorithms based on multi-feature fusion mainly comprises the steps:
S1, acquisition training data；Training process is as shown in Figure 2；
S1.1, by the water meter image pattern in a large amount of actual scene of RGB camera collection, including various light According to, visual angle, water meter type, water meter extent of damage etc., to ensure the multiformity of sample；
S1.2, the meter reading region in water meter image pattern acquired in S1.1 is carried out artificial mark, including The center in meter reading region (x, y), length (h), width (w) and angle (a)；
S2 training objective grader:
S2.1, mark according to S1 gained water meter image and reading area, cut out reading area and non-reading, for target Classification；
S2.2, the multi-channel feature of extraction S2.1 institute cutting image, with this feature for input, train integrated decision tree classification Whether device is to classify in reading area to this region；
S3 calculates image multi-channel feature:
S3.1, extract water meter image multi-channel feature, including gradient orientation histogram, gradient magnitude, LUV color characteristic, Greyscale color feature, and calculate characteristic-integration figure:
Gradient direction computational methods:
Gradient magnitude computational methods:
S4 sliding window scans:
S4.1, travel through each sliding window, utilize S3.1 gained characteristic-integration figure, calculate the multi-channel feature of each sliding window, and Carry out Feature Fusion；
S4.2, with S4.1 merge characteristic vector for input, utilize S2 training gained grader sliding window is classified, Obtain sliding window significance；
S4.3, the target detected is carried out maximization suppression, obtain detect target.
S5 extrapolation Analysis On Multi-scale Features:
S5.1, utilize the statistical property of adjacent scalogram picture, by the method for extrapolation estimate multiple dimensioned under artwork feature, Extrapolation algorithm is as follows:
C S ≈ R ( C , s ) · s - λ Ω ,
The wherein yardstick of S representative image, CsRepresenting the characteristics of image under yardstick S, (C is s) that characteristic pattern C is carried out yardstick to R Resampling for S；λ is the channel parameters that the statistical analysis by image obtains；Ω represents different image channels；Extrapolation algorithm The characteristics of image of multiple adjacent yardstick is quickly calculated by the characteristics of image calculated under a certain fixed size；
S5.2, repeat S4, S5, obtain multiple dimensioned under reading area target window.
S6 rotates detection:
S6.1, with 5 degree for interval, multiple rotary original water gauge image, repeat S3, S4, S5, S6 step, can be calculated Multidirectional meter reading region.
Above-described embodiment is the present invention preferably embodiment, but embodiments of the present invention are not by above-described embodiment Limit, the change made under other any spirit without departing from the present invention and principle, modify, substitute, combine, simplify, All should be the substitute mode of equivalence, within being included in protection scope of the present invention.

Claims (8)

1. a meter reading Region detection algorithms based on multi-feature fusion, it is characterised in that comprise the steps of
S1, acquisition training data, shoot water meter image pattern by photographic head, and the meter reading region in image carried out people Work marks, and obtains the center of meter reading, length and width information；
Reading area and non-reading area in S2, cutting water meter image, extract the multi-channel feature of this cutting zone and carry out spy Levy fusion, with extract multi-channel feature for input training image grader；Described cutting zone is uncertain region, by scheming As grader is classified, it is divided into reading area and non-reading area；
S3, extraction water meter image multi-channel feature, described multi-channel feature includes gradient orientation histogram, gradient magnitude, LUV face Color characteristic, greyscale color feature, calculate the characteristic-integration figure of water meter image；
S4, travel through whole sliding window, utilize characteristic-integration figure to calculate each sliding window feature, utilize the Image Classifier pair that S2 trains Sliding window is classified, and obtains target window；
S5, with extrapolation method estimate artwork characteristic-integration figure under multiple yardsticks, repeat S4, S5 step, obtain multiple dimensioned Target window；
S6, rotation artwork, repeat S3, S4, S5, S6 step, obtain multidirectional target window.
Meter reading Region detection algorithms the most based on multi-feature fusion, it is characterised in that described step Rapid S1 particularly as follows:
S1.1, by the water meter image pattern in RGB camera collection actual scene；
S1.2, the meter reading region in water meter image pattern acquired in S1.1 is carried out artificial mark, including water meter The center of reading area (x, y), length h, width w and angle a.
Meter reading Region detection algorithms the most based on multi-feature fusion, it is characterised in that described water Table image pattern includes following different parameter: illumination, visual angle, water meter type, the water meter extent of damage.
Meter reading Region detection algorithms the most based on multi-feature fusion, it is characterised in that described step Rapid S2 particularly as follows:
S2.1, mark according to step S1 gained water meter image and reading area, cut out reading area and non-reading, for target Classification；
S2.2, the multi-channel feature of extraction S2.1 institute cutting image, with multi-channel feature for input, train integrated decision tree classification Whether device is to classify in reading area to this region.
Meter reading Region detection algorithms the most based on multi-feature fusion, it is characterised in that described step Rapid S3 particularly as follows:
S3.1, extraction water meter image multi-channel feature, including gradient orientation histogram, gradient magnitude, LUV color characteristic, gray scale Color characteristic, and calculate characteristic-integration figure；
The computational methods of described gradient direction:
Wherein (i j) is pixel (i, j) angle of place's gradient direction to O；I is image, and x represents horizontal direction, and y represents Vertical Square To, i represents pixel coordinate in the horizontal direction；J represents pixel coordinate in vertical direction；
The computational methods of described gradient magnitude:
Wherein (i j) is the gradient magnitude at pixel (i.j) place to M；I is image, and x represents horizontal direction, and y represents vertical direction, i Represent pixel coordinate in the horizontal direction；J represents pixel coordinate in vertical direction.
Meter reading Region detection algorithms the most based on multi-feature fusion, it is characterised in that described step Rapid S4 particularly as follows:
S4.1, travel through each sliding window, utilize step S3 gained characteristic-integration figure, calculate the multi-channel feature of each sliding window, go forward side by side Row Feature Fusion；
S4.2, with S4.1 merge characteristic vector for input, utilize S2 training gained Image Classifier sliding window is classified, Obtain sliding window significance；
S4.3, the target detected is carried out maximization suppression, obtain detect target.
Meter reading Region detection algorithms the most based on multi-feature fusion, it is characterised in that described step Rapid S5 particularly as follows:
S5.1, utilize the statistical property of adjacent scalogram picture, by the method for extrapolation estimate multiple dimensioned under artwork feature, extrapolation Algorithm is as follows:
C s ≈ R ( C , s ) · s - λ Ω ,
The wherein yardstick of S representative image, CsRepresenting the characteristics of image under yardstick S, (C, s) characteristic pattern C carries out yardstick is S to R Resampling；λ is the channel parameters that the statistical analysis by image obtains；Ω represents different image channels；Extrapolation algorithm passes through The characteristics of image calculated under a certain fixed size quickly calculates the characteristics of image of multiple adjacent yardstick；
S5.2, repeat S4, S5, obtain multiple dimensioned under reading area target window.
Meter reading Region detection algorithms the most based on multi-feature fusion, it is characterised in that described step Rapid S6 particularly as follows:
With 5 degree for interval, multiple rotary original water gauge image, repeats S3, S4, S5, S6 step, is calculated multidirectional water meter reading Number region, i.e. obtains multidirectional target window.
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