CN109145746A - A kind of signal lamp detection method based on image procossing - Google Patents
A kind of signal lamp detection method based on image procossing Download PDFInfo
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Abstract
The signal lamp detection method based on image procossing that the invention discloses a kind of, includes the following steps: step 1: reading image file F from front end camera0；Step 2: reading the area-of-interest in system configuration file, and from F0In copy region of interest area image F1；Step 3: according to the current time in system to image F1Carry out image enhancement；Step 4: to image F2Color region segmentation is carried out, step 5: to image F4Carry out morphologic closed operation processing；Step 6: extracting image F4In connected region, step 7: extract CLThe boundary rectangle in middle region, and according to boundary rectangle position from image F2Middle copy candidate signal lamp image, obtains signal lamp image collection L；Step 8: extracting the Hog feature of all images in L, the invention has the advantages that being able to achieve the signal lamp detection of complex scene, and meet the stability of system and the demand of real-time simultaneously.
Description
Technical field
The present invention relates to technical field of image processing, and in particular to a kind of signal lamp detection method based on image procossing.
Background technique
With the rapid development of internet and hardware art, the artificial intelligence epoch have arrived.Researcher gradually starts
The problems in daily life is solved using various related advanced technologies, so that it is growing to quality of life to meet people
It pursues.Intelligent transportation system is exactly one of this product of the time, it together, mentions the technological incorporation of multiple fields for people
For safety and convenience.And key components of the detection of traffic lights as intelligent transportation system, there is huge research
Value and potentiality, but since signal lamp detection has higher requirement to method robustness, energy stable operation is in complicated road day
In gas field scape, and requirement of real-time is higher, and most detection methods can not meet above-mentioned requirements simultaneously.Therefore it is based on image
The signal lamp detection method of processing is a kind of preferable solution, has not only been able to satisfy the requirement of real-time of signal lamp detection, but also energy
Stable operation is in the weather scene of Various Complex.
For the accurate detection property and the problem of real-time of signal lamp, domestic and international academia, industry propose many schemes.
The technical solution being wherein closer to the present invention includes: that (Wu Ying, Zhang little Ning, He Bin are believed Wu Ying based on the traffic of image procossing
Signal lamp recognition methods [J] traffic information and safety, 2011,29 (3): 51-54.) using the good HSI of complex but stability
Color space carries out the segmentation of image, by calculating circularity and traffic lights bottom plate length-width ratio, filters candidate region, finally
Traffic signals lamp type is confirmed by template matching.But algorithm accuracy in complex scene is not high, and needs to know in advance
, there are many restrictions in practical applications in the placement direction of road traffic lights.Gu Mingqin (Gu Mingqin, Cai Zixing, Huang Zhenwei,
Real-time recognizer [J] Central South University's journal (natural science edition) of arrowhead-shaped traffic lights in equal urban environment, 2013,
44 (4): 1403-1408.) image under RGB color is transformed into YCbCr color space, recycle Gabor wavelet transformation
The feature of traffic lights candidate region is obtained with two-dimentional independent component analysis, finally with nearest neighbor classifier to traffic lights
Type identified.Since the conversion of RGB color to YCbCr color space is linear, so the algorithm real-time
Relatively good, under the relatively good environment of light condition on daytime, recognition accuracy is relatively high, but is not suitable for night environment still.
(Zhou Xuanru, Yuan Jiazheng, Liu Hongzhe wait based on real-time recognizer research [J] of the traffic lights of HOG feature to Zhou Xuanru
Calculation machine science, 2014,41 (7): 313-317.) algorithm that proposes carries out image segmentation also in YCbCr color space, then
Obtain traffic lights candidate region by area, shape, density triple filter, then after extraction process image HOG feature,
It is identified using SVM classifier.The method that algorithm uses machine learning, can carry out traffic in more complicated environment
The detection of signal lamp, while round traffic lights can be detected but also detect arrow-shaped traffic lights, but for having
The traffic lights recognition effect of number is bad.
In conclusion existing in current signal light detection scheme following insufficient: 1) algorithm accuracy is not high, may not apply to
Complicated weather scene；2) traffic lights various shapes, algorithm cannot be suitable for a variety of traffic signals of different shapes simultaneously
Lamp inspection is surveyed；3) most of traffic lights detection algorithms are detected for the traffic scene on daytime, to the signal lamp inspection at night
It is poor to survey effect.
Traffic lights identification is the base application of wisdom traffic system, determines the accurate of break in traffic rules and regulations and traffic scheduling
Property and real-time, but from the point of view of existing achievement, intelligent transportation system is also and not perfect, and the detection of traffic lights and identification
Also there are many areas for improvement for key components as system.From the point of view of current many achievements, in traffic signals
Lamp context of detection, for color and shape as most important two features of signal lamp, many algorithms are unfolded around them,
But since the detection method is higher to real-time and stability requirement, most algorithm, which can not meet simultaneously the two, to be wanted
It asks, and by image enhancement, color segmentation, Morphological scale-space, geometrical characteristic filtering and to signal lamp area in image in the present invention
Domain extracts and passes through machine learning method marker lamp, is able to achieve the signal lamp detection of complex scene, and meets simultaneously
The stability of system and the demand of real-time.
Summary of the invention
In order to solve the problems in the existing technology, the present invention provides accurate, real-time a kind of based on image procossing
Signal lamp detection method.
Technical scheme is as follows:
A kind of signal lamp detection method based on image procossing, which comprises the steps of:
Step 1: reading image file F from front end camera0；
Step 2: reading the area-of-interest in system configuration file, and from F0In copy region of interest area image F1；
Step 3: according to the current time in system to image F1Carry out image enhancement；6:00 between 18:00 be daytime, it is right
Image F1Histogram equalization is carried out, remaining time is night, to F1Carry out gamma correction, image F after must enhancing2；
Step 4: to image F2Carry out color region segmentation, the specific steps are as follows:
Step 4.1 is by image F2It is transformed into hsv color space；
Step 4.2 is according to system time to F2Color threshold segmentation is carried out, Pixel Information in set red and green is retained,
And the lightness of other pixels is set as 0, obtain Threshold segmentation image F3；Retain the pixel value for meeting the pixel of formula (1) daytime；
Night retains the pixel value for meeting the pixel of formula (2)；
In formula, p (x, y) indicates location of pixels as (x, y) pixel, and h, s, v respectively indicate the point in Color Channel HSV
The channel value in each channel, red indicate red pixel point set, and green indicates green pixel point set；
Step 4.3 is to image F3Gray processing is carried out, and binaryzation is carried out by OTSU, obtains binary image F4；
Step 5: to image F4Carry out morphologic closed operation processing；
Step 6: extracting image F4In connected region, obtain connected region set C={ Ai| i=1,2,3 ..., n }, AiTable
Show i-th of connected region, n indicates total connected region quantity；According to formula (3) (4) (5), to object in C, it carries out geometrical characteristic mistake
Filter, ungratified object is rejected from C, obtains candidate signal lamp regional ensemble CL；
Smin< Ai.S < Smax (3)
rwhmin< Ai.rwh< rwhmax (4)
Kmin< Ai.K < Kmax (5)
Wherein, wherein SminAnd SmaxRespectively indicate minimum and maximum area threshold given in advance, Ai.S A is indicatediRegion
Area；rwhminAnd rwhmaxRespectively indicate minimum and maximum the ratio of width to height given in advance, Ai.rwhIndicate AiRegion the ratio of width to height；Kmin
And KmaxRespectively indicate minimum and maximum areal concentration given in advance, Ai.K A is indicatediAreal concentration, i.e. AiIn connected region
Pixel quantity and the region minimum circumscribed rectangle area ratio；
Step 7: extracting CLThe boundary rectangle in middle region, and according to boundary rectangle position from image F2Middle copy candidate signal
Lamp image obtains signal lamp image collection L；
Step 8: the Hog feature of all images in L is extracted, and Feature Dimension Reduction is carried out to it by PCA method, finally by
Trained SVM classifier identifies signal lamp according to characteristics of image after dimensionality reduction.
The beneficial effects of the present invention are: guaranteeing that traffic lights have good detection effect under complex environment；According to letter
The priori features such as signal lamp shape color carry out rapidly extracting to signal lamp region, and are dropped by PCA method to Hog feature
Dimension, improves the speed of algorithm, is able to satisfy the real-time demand of system；The image of day and night is handled respectively, it can be same
When meet day and night difference illumination scene signal lamp detection, have higher robustness；It being capable of real-time detection various shapes
With the traffic lights of state, and accuracy with higher.
Detailed description of the invention
Fig. 1 is that the present invention is based on the signal lamp detection method flow charts of image procossing；
Fig. 2 is histogram equalization on daytime front and back comparison diagram in step 3 in the present invention；
Fig. 3 is comparison diagram before and after night gamma correction in step 3 in the present invention；
Fig. 4 is closing operation of mathematical morphology comparison diagram before and after the processing in step 4 in the present invention；
Fig. 5 is the signal lamp result schematic diagram obtained after step 8 in the present invention.
Specific embodiment
Elaborate that the present invention is based on the specific implementations of the signal lamp detection method of image procossing below with reference to embodiment
Mode.
Specific step is as follows for a kind of signal lamp detection method based on image procossing:
Step 1: reading image file F from front end camera0；
Step 2: reading the area-of-interest in system configuration file, and from F0In copy region of interest area image F1；
Step 3: according to the current time in system to image F1Carry out image enhancement；6:00 between 18:00 be daytime, it is right
Image F1Histogram equalization is carried out, remaining time is night, to F1Carry out gamma correction, image F after must enhancing2；
Step 4: to image F2Carry out color region segmentation, the specific steps are as follows:
Step 4.1 is by image F2It is transformed into hsv color space；
Step 4.2 is according to system time to F2Color threshold segmentation is carried out, Pixel Information in set red and green is retained,
And the lightness of other pixels is set as 0, obtain Threshold segmentation image F3；Retain the pixel value for meeting the pixel of formula (1) daytime；
Night retains the pixel value for meeting the pixel of formula (2)；
In formula, p (x, y) indicates location of pixels as (x, y) pixel, and h, s, v respectively indicate the point in Color Channel HSV
The channel value in each channel, red indicate red pixel point set, and green indicates green pixel point set；
Step 4.3 is to image F3Gray processing is carried out, and binaryzation is carried out by OTSU, obtains binary image F4；
Step 5: to image F4Carry out morphologic closed operation processing；
Step 6: extracting image F4In connected region, obtain connected region set C={ Ai| i=1,2,3 ..., n }, AiTable
Show i-th of connected region, n indicates total connected region quantity；According to formula (3) (4) (5), to object in C, it carries out geometrical characteristic mistake
Filter, ungratified object is rejected from C, obtains candidate signal lamp regional ensemble CL；
Smin< Ai.S < Smax (3)
rwhmin< Ai.rwh< rwhmax (4)
Kmin< Ai.K < Kmax (5)
Wherein, wherein SminAnd SmaxRespectively indicate minimum and maximum area threshold given in advance, Ai.S A is indicatediRegion
Area；rwhminAnd rwhmaxRespectively indicate minimum and maximum the ratio of width to height given in advance, Ai.rwhIndicate AiRegion the ratio of width to height；Kmin
And KmaxRespectively indicate minimum and maximum areal concentration given in advance, Ai.K A is indicatediAreal concentration, i.e. AiIn connected region
Pixel quantity and the region minimum circumscribed rectangle area ratio；In this example, Smin=30, Smax=120；rwhmin=
0.5, rwhmax=2.0；Kmin=0.5, Kmax=0.9；
Step 7: extracting CLThe boundary rectangle in middle region, and according to boundary rectangle position from image F2Middle copy candidate signal
Lamp image obtains signal lamp image collection L；
Step 8: the Hog feature of all images in L is extracted, and Feature Dimension Reduction is carried out to it by PCA method, finally by
Trained SVM classifier identifies signal lamp according to characteristics of image after dimensionality reduction.
Content described in this specification embodiment is only enumerating to the way of realization of inventive concept, protection of the invention
Range should not be construed as being limited to the specific forms stated in the embodiments, and protection scope of the present invention is also and in this field skill
Art personnel conceive according to the present invention it is conceivable that equivalent technologies mean.
Claims (1)
1. a kind of signal lamp detection method based on image procossing, which comprises the steps of:
Step 1: reading image file F from front end camera0；
Step 2: reading the area-of-interest in system configuration file, and from F0In copy region of interest area image F1；
Step 3: according to the current time in system to image F1Carry out image enhancement；In 6:00 to being daytime between 18:00, to image
F1Histogram equalization is carried out, remaining time is night, to F1Carry out gamma correction, image F after must enhancing2；
Step 4: to image F2Carry out color region segmentation, the specific steps are as follows:
Step 4.1 is by image F2It is transformed into hsv color space；
Step 4.2 is according to system time to F2Color threshold segmentation is carried out, retains Pixel Information in set red and green, and will
The lightness of other pixels is set as 0, obtains Threshold segmentation image F3；Retain the pixel value for meeting the pixel of formula (1) daytime；Night,
Retain the pixel value for meeting the pixel of formula (2)；
In formula, p (x, y) indicates that location of pixels is (x, y) pixel, and h, s, it is each logical in Color Channel HSV that v respectively indicates the point
The channel value in road, red indicate red pixel point set, and green indicates green pixel point set；
Step 4.3 is to image F3Gray processing is carried out, and binaryzation is carried out by OTSU, obtains binary image F4；
Step 5: to image F4Carry out morphologic closed operation processing；
Step 6: extracting image F4In connected region, obtain connected region set C={ Ai| i=1,2,3 ..., n }, AiIndicate i-th
A connected region, n indicate total connected region quantity；According to formula (3) (4) (5), to object in C, it carries out geometrical characteristic filtering, will
Ungratified object is rejected from C, obtains candidate signal lamp regional ensemble CL；
Smin< Ai.S < Smax (3)
rwhmin< Ai.rwh< rwhmax (4)
Kmin< Ai.K < Kmax (5)
Wherein, wherein SminAnd SmaxRespectively indicate minimum and maximum area threshold given in advance, Ai.S A is indicatediArea surface
Product；rwhminAnd rwhmaxRespectively indicate minimum and maximum the ratio of width to height given in advance, Ai.rwhIndicate AiRegion the ratio of width to height；KminWith
KmaxRespectively indicate minimum and maximum areal concentration given in advance, Ai.K A is indicatediAreal concentration, i.e. AiIn connected region
The ratio of pixel quantity and the minimum circumscribed rectangle area in the region；
Step 7: extracting CLThe boundary rectangle in middle region, and according to boundary rectangle position from image F2Middle copy candidate signal lamp figure
Picture obtains signal lamp image collection L；
Step 8: extracting the Hog feature of all images in L, and Feature Dimension Reduction is carried out to it by PCA method, finally by training
Good SVM classifier identifies signal lamp according to characteristics of image after dimensionality reduction.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111639656A (en) * | 2020-05-28 | 2020-09-08 | 东软睿驰汽车技术（沈阳）有限公司 | Traffic signal lamp identification method and device |
| CN111784709A (en) * | 2020-07-07 | 2020-10-16 | 北京字节跳动网络技术有限公司 | Image processing method, image processing device, electronic equipment and computer readable medium |
| CN111784710A (en) * | 2020-07-07 | 2020-10-16 | 北京字节跳动网络技术有限公司 | Image processing method, image processing apparatus, electronic device, and medium |
| CN112215089A (en) * | 2020-09-21 | 2021-01-12 | 卡斯柯信号有限公司 | Video identification method of subway color light signal machine |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103489324A (en) * | 2013-09-22 | 2014-01-01 | 北京联合大学 | Real-time dynamic traffic light detection identification method based on unmanned driving |
| CN104021378A (en) * | 2014-06-07 | 2014-09-03 | 北京联合大学 | Real-time traffic light recognition method based on space-time correlation and priori knowledge |
| CN104851288A (en) * | 2015-04-16 | 2015-08-19 | 宁波中国科学院信息技术应用研究院 | Traffic light positioning method |
| CN106991707A (en) * | 2017-05-27 | 2017-07-28 | 浙江宇视科技有限公司 | A kind of traffic lights image intensification method and device based on imaging features round the clock |
| CN107507142A (en) * | 2017-08-11 | 2017-12-22 | 江苏航天大为科技股份有限公司 | Signal lamp image processing method |
-
2018
- 2018-07-20 CN CN201810804266.XA patent/CN109145746B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103489324A (en) * | 2013-09-22 | 2014-01-01 | 北京联合大学 | Real-time dynamic traffic light detection identification method based on unmanned driving |
| CN104021378A (en) * | 2014-06-07 | 2014-09-03 | 北京联合大学 | Real-time traffic light recognition method based on space-time correlation and priori knowledge |
| CN104851288A (en) * | 2015-04-16 | 2015-08-19 | 宁波中国科学院信息技术应用研究院 | Traffic light positioning method |
| CN106991707A (en) * | 2017-05-27 | 2017-07-28 | 浙江宇视科技有限公司 | A kind of traffic lights image intensification method and device based on imaging features round the clock |
| CN107507142A (en) * | 2017-08-11 | 2017-12-22 | 江苏航天大为科技股份有限公司 | Signal lamp image processing method |
Non-Patent Citations (1)
| Title |
|---|
| 纪甫娟: "自然场景下交通标志检测算法的研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111639656A (en) * | 2020-05-28 | 2020-09-08 | 东软睿驰汽车技术（沈阳）有限公司 | Traffic signal lamp identification method and device |
| CN111784709A (en) * | 2020-07-07 | 2020-10-16 | 北京字节跳动网络技术有限公司 | Image processing method, image processing device, electronic equipment and computer readable medium |
| CN111784710A (en) * | 2020-07-07 | 2020-10-16 | 北京字节跳动网络技术有限公司 | Image processing method, image processing apparatus, electronic device, and medium |
| CN111784709B (en) * | 2020-07-07 | 2023-02-17 | 北京字节跳动网络技术有限公司 | Image processing method, image processing device, electronic equipment and computer readable medium |
| CN111784710B (en) * | 2020-07-07 | 2023-10-20 | 抖音视界有限公司 | Image processing method, device, electronic equipment and medium |
| CN112215089A (en) * | 2020-09-21 | 2021-01-12 | 卡斯柯信号有限公司 | Video identification method of subway color light signal machine |
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