RESEARCH Open Access

The research on edge detection algorithmof laneZhong-xun Wang* and Wenqi Wang

Abstract

In developed countries, the automation and intelligent development of vehicles has reached a relatively high leveland has gradually developed in China. In recent years, algorithms for lane detection have emerged in an endlessstream, but the advantages and disadvantages of comprehensive comparison of various algorithms have resultedin the following problems: First, the robustness of lane line detection is poor, mainly due to the surrounding environmentof the road. The impact is greater, in traffic-intensive city streets, affected by natural factors such as trees or building shadowsaround the driveway; second, the real-time nature of the lane line detection is poor, affected by other marking lines on thedriveway, or damaged in the lane. When the pollution is serious, the image of the lane line collected by the system isincomplete and of poor quality, which increases the difficulty of analyzing and processing data of the detection system. Thisarticle aims at the problems existing in the current lane line detection and determines the research focus of the article: (1)Improve the accuracy and real-time performance of the detection algorithm. Most of the factors affecting the detection ofthe lane line are generated before the image is acquired. This requires the strict pre-processing of the collected lane lineimage to remove a large amount of interference. After the information, not only can the detection result be more accuratebut also the complexity of the algorithm be simplified, making the detection result accurate and effective. (2) Use the FPGAfor verification. This paper simulates the detection algorithm from two aspects of MATLAB and FPGA, learns the workingmode of the related chip and different interface protocols, optimizes the logic design through the hardware designlanguage, and facilitates the hardware implementation of the algorithm. This method can effectively improve the imageprocessing speed, save the logic resources, and better realize the lane recognition function. (3) Improve the existing lane linedetection algorithm. There are many algorithms for lane detection, but each algorithm has its own advantages anddisadvantages. Part of this article summarizes the advantages and disadvantages of these detection algorithms, analyzestheir feasibility in actual detection, and improves the algorithm based on this.

Keywords: Lane line detection and identification, Kirsch algorithm, MATLAB

1 IntroductionThe automotive industry is one of the largest and mostimportant industries in the world. According to statisticsfrom China Association of Automobile Manufacturers in2016, the total number of automobile production andsales in China was 28.119 million vehicles and 28.058million vehicles, a record high [1].With the constant popularity of automobiles and the con-

tinuous improvement of people’s living standards, almostevery family owns one or more cars, providing people witha comfortable and convenient travel life. Although enteringthe twenty-first century, the post-modern transportation

system has been very developed, but it is still not in directproportion to the increasing traffic environment demand ofpeople. The rapid development of the automotive industryhas also brought with it environmental pollution, energyshortages, traffic jams, and traffic safety issues, among whichtraffic safety issues are particularly acute and thorny andhave developed into a major global problem [2].In China, the number of casualties caused by road

traffic safety exceeds 200,000 each year, and the totalnumber of traffic accidents handled is approximately 4.7million people [3]. Research shows that the number ofcasualties and property losses caused by traffic accidentswill continue to increase substantially in the comingyears, and the losses caused by road traffic will becomeone of the three major factors leading to global disease

* Correspondence: ytuwwq@126.comInstitute of Science and Technology for Opto-Electronics Information, YantaiUniversity, Yantai 264005, Shandong, China

EURASIP Journal on Imageand Video Processing

© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made.

Wang and Wang EURASIP Journal on Image and Video Processing (2018) 2018:98 https://doi.org/10.1186/s13640-018-0326-2

and injury in 2020 [4]. The causes of comprehensive traf-fic accidents mainly include four aspects: the car’s ownperformance, drivers, roads, and environment. Amongthese four aspects, the driver’s personality, fatigue driving,and drunk driving are the most common causes of acci-dents. When the car is in motion, the driver dominates,and the driver’s driving operation directly determines thestability and safety of the vehicle during driving.According to the statistics of traffic accidents, the human

factor accounted for 95% of all the factors that caused theaccident, and the accident rate caused by the driver’s miso-peration was as high as 73%. This further indicates that thedriver’s misoperation is a traffic accident and the main fac-tor that occurs [5].Faced with such severe traffic safety problems, the

development of vehicle safety technology has become animportant research topic in order to reduce the number ofpeople injured or killed in car accidents.The current advanced car safety technology is mainly

through computer, automatic control, and information fu-sion methods to improve the driver’s driving safety factor,making the car more intelligent in the driving process. Asa result, research on intelligent transportation systemsbegan to develop. Based on the intelligent perception ofthe vehicle’s driving environment, when the driver oper-ates improperly, the system will automatically warn thedriver through voice and other forms to correct thecurrent erroneous driving operation. The system will alsoautomatically replace the driver’s control of the vehicle inspecial circumstances such as the driver’s sleep or poormental status, thereby avoiding traffic accidents.Smart cars occupy the leading position in the intelligent

transportation system. It not only includes the function ofsensing environment and decision-making planning butalso completes multi-level assisted driving functions. It isa brand new comprehensive high-tech system [6, 7]. Rely-ing on its own sensors, smart cars can fully perceive thesurrounding environmental information, which is a pre-requisite for other functions.However, there are no two identical roads in the world.

This is because there are too many influencing factors onthe road conditions. When natural and non-natural fac-tors change, it is easy for drivers to make wrong drivingjudgments on specific road condition information whichleads to traffic accident. If the vehicle can be judged incor-rectly by the driver at this time, the correct judgment ofthe road conditions and timely warning to guide thedriver’s wrong operation will be able to effectively avoidthe occurrence of traffic accidents. At this time, the smartcar system is required to accurately judge the road condi-tions and feedback correct driving information to thedriver [8]. Unmanned vehicles have already begun trial op-eration on a global scale. How to detect and identify roadsigns, obstacles, and pedestrians in an unknown environment

in an accurate and real-time manner is the focus of thecurrent research on unmanned vehicles [9].To sum up, the in-depth study of lane detection and

recognition technology has important application valueand practical significance.

2 Difficulty and insufficiency of lane detectionReal-time and robustness are the purpose of lane detec-tion. When the vehicle is driving at a certain speed, theroad image collected must be processed in time to ensurethe normal driving of the vehicle. The entire lane line de-tection system not only needs to adapt to the complicatedand changeable road environment but also adapts to theinfluence of some force majeure factors. The specific con-ditions are summarized as follows:

1. Most existing lane line detection algorithms aremainly designed for structured roads and notstructured roads, due to the influence of thesurrounding environment of the road, such as theinfluence of shadows of surrounding trees orbuildings and the pair of vehicles ahead of the road.The influence of the lane line shielding, etc. makesthe detection of the lane line very poor.

2. When affected by other markers on the driveway,or when the lane is damaged or the pollution isserious, the image of the lane line collected by thesystem is incomplete and of poor quality, making itdifficult for the system to accurately judge andanalyze the data, and it is impossible to guaranteethe correctness of the data and real-time detectionof lane lines.

Unclear detection and multi-peak detection problemsare the biggest problems faced by traditional Hough trans-forms and are severely affected by noise. After the Houghtransform is improved, the robustness and adaptability ofthe detection result are enhanced. In order to overcomethe weather changes and the camera lens is not clean, thispaper uses the two-dimensional median filtering methodto process the original image.In the edge detection stage, the Kirsch algorithm has ob-

vious superiority, and this paper upgrades on the basis ofthe original classical algorithm, which makes the imageprocessing speed more than twice the original, expandsthe scope of use, and improves the detection and identifi-cation of lane lines. Speed, and while speeding up, doesnot affect the detection of lane lines.

3 Demand analysis of lane detection andidentificationThe detection and identification of the lane line is torealize the intelligence and automation of the modern

Wang and Wang EURASIP Journal on Image and Video Processing (2018) 2018:98 Page 2 of 9

transportation system. It has a good application in thefields of advanced parking assistance, lane keeping assist-ance, off-road warning, and lane change assistance.Demand analysis is a necessary step in system design. It

can not only understand the operating mechanism of thesystem before design but also adjust the design structure intime for the demand. For the lane line identification, the ul-timate goal is to accurately and efficiently identify the laneline. For this purpose, do the following needs analysis:

(1) Accuracy

The purpose of intelligent transportation system designis to reduce the rate of urban traffic accidents, so accuracyis the primary requirement of system design. If there is anerror or deviation in the detection of the lane line, it maycause the vehicle to head in the wrong direction, which isnot conducive to its own driving safety and brings greatersafety risks to road safety.

(2) High efficiency

When the vehicle is driving on the road, it must not onlybe able to accurately follow the direction of the lane linebut also be able to maintain a certain speed. This requiresboth the detection accuracy and the detection in the laneline identification process efficiently and in real-time.

(3) Memory

In the intelligent public transport system, in addition tothe vehicle that can detect the lane line image in real time, italso has a good memory function, which can effectively storeand manage the recorded data information and facilitate in-vestigation and evidence collection after a traffic accident.

(4) Simple design structure

The system design is mainly used in vehicles, and theoccupied space should be as small as possible. You canmake full use of 5G communication real-time connec-tion without affecting other functions of the vehicle.

(5) Fully automated

The system is designed to allow the driver to alert andcorrect the driver’s driving errors in an unconscious situ-ation, so it is necessary to be able to fully automate the sys-tem during operation.

4 The proposed method—lane line detection andidentificationThe lane detection and recognition process consists ofthe following components: image acquisition, image pre-processing, image segmentation, edge detection, featureextraction, feature point identification, and lane line rec-ognition, as shown in Fig. 1.

4.1 Road image acquisition and pretreatmentIn the lane line detection process, the image processedby the system is mainly captured by the car’s camera.The pictures used in this article are mainly shot on aCanon EOS 100D DSLR camera. In the smart car identi-fication and navigation system based on image process-ing, due to the interference of external factors in theactual road conditions, such as the influence of opticalfiber, weather, roads, and the surrounding environment,the images collected by the smart car camera includespots, pits, etc. The interference factors and the declineof image quality will directly affect the detection andrecognition of the target information lane line [10].Therefore, it is necessary to preprocess the collectedimages, reduce the useless interference information and en-hance the target information, simplify the image processingalgorithm, and improve the detection and recognitionaccuracy of lane lines.The image of the lane line is taken from the real scene

shooting. The speed of the vehicle traveling on the road isrelatively fast. When shooting the lane line image, it canonly be shot on the zebra crossing when the red light is on.For safety reasons, consider turning left and right. Thevehicle needs to be fast and accurate when shooting. Thiscamera is also chosen to achieve better focus in a shorttime, improve focus speed, and ensure image quality.Due to the influence of actual road conditions and

other external disturbance factors, the images collectedby the smart car camera contain various interferencefactors such as spots and pits, and the degradation ofimage quality will directly affect the detection and

Collect images Pretreatment

Featureextraction

Feature pointrecognition

Feature pointrecognition

Edge detectionSplit the image

Fig. 1 Lane detection and recognition process

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recognition of the target information lane line. There-fore, image preprocessing is a necessary step before ana-lyzing and judging image information.The purpose of image preprocessing is to remove the noise

in the image, save the useful road surface information rea-sonably, minimize the useless information in the road surfaceinformation, and strengthen the target information, therebysimplifying the algorithm complexity of image processing.The image preprocessing part of this paper mainly in-

cludes road image graying, median filter method noise re-duction, and image edge enhancement. Image grayingreduces the amount of color information in the image andsimplifies the complexity of the algorithm; two-dimensionalwavelet packet decomposition technology can adaptivelyselect the frequency band and improve the resolution of thefrequency band; and image enhancement highlightsthe characteristics of the region of interest and can en-hance the grayscale of the image. In contrast, lane lineinformation strengthens.The image captured by the smart car camera is a color

image containing a lot of extraneous information. Be-cause the grayscale image contains less information, thelane line detection algorithm usually directly processesthe grayscale image to speed up the processing speed ofthe later detection algorithm. In the RGB color space,the color of the image is different according to the threeprimary colors and proportional mixing results [11]. To

convert an RGB color image to a grayscale image, the lu-minance value is quantized. There are generally fourmethods for graying a color image: the average method,the component method, the maximum method, and theweighted average method. The weighted average methodis selected here for graying. Equation (1) shows thetransformation relationship, and the processed lane lineeffect diagram is shown in Fig. 2.

V gray ¼ 0:290Rþ 0:587G þ 0:116BV gray ¼ R ¼ G ¼ B

�ð1Þ

Salt and pepper noise is the most common and mainsource of noise in lane line images. It is generally charac-terized by black-white impulse noise points, which tendsto blur the road image and flood the feature information,which brings great difficulties to subsequent processing.Therefore, it is necessary to filter out noise to improveimage quality. The principle of image filtering processingis to preserve the original useful information in the imagewhile removing the noise. After comparing various filter-ing methods, it is found that the median filtering has thebest filtering effect on the salt and pepper noise, whichcan overcome the edge blurring phenomenon and can bebetter, preserve original image contour information, andhas good real-time performance.

(a) (b)

(c) (d)Fig. 2 After the processing of the lane line effect map. a Salt and pepper noise. b Mean filter image. c Gaussian filter image. d Median filter image

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After filtering, there is still a lot of interference informa-tion for lane lines to be identified. The main purpose of theimage edge enhancement is to emphasize the contour in-formation of the lane line as much as possible based on thegrayscale features of the lane line edge in the grayscaleimage and to reduce the interference caused by othernon-lane line object contours [12, 13]. After image edge en-hancement, detection and recognition of subsequent lanelines are made more accurate. The comparison of differentenhancement methods shows that the Laplacian-basedimage enhancement method can not only enhance thehigh-frequency components of the image but also preservethe low-frequency components of the image, which is an ef-fective method for image edge enhancement [14].

4.2 Lane line edge detectionThere are many types of road traffic signs in China. The reg-ulations of the People’s Republic of ChinaTraffic Road Mark-ing Line clearly define the color and shape characteristics ofChina’s traffic signs. In terms of color, there are two kinds ofpure white and pure yellow. The pure white marking line isdivided into white solid line and white dashed line to distin-guish different lanes in the same direction. The pure yellowmarking line is further divided into double yellow line, singleyellow line, yellow solid line, yellow dotted line, etc., whichare used to distinguish different directions of the lane; interms of shape, for the lane line, it is mainly divided intosolid line, dotted line, and straight line and so as for thecurve two.

In the process of actually detecting the lane line, it willbe affected by other traffic signs, light, and color fading ofthe road. However, according to the actual image col-lected, the color difference between the lane line and theroad surface is still obvious, as shown in Fig. 3. Therefore,the color can be used as one of the feature information fordetecting the lane line.The edge of the image is the singularity and mutation

point of the grayscale function of the image, that is, thearea of the image where the grayscale informationchanges drastically. Along the pixels at the edges of theimage, the gray value changes slowly, and the pixels thatare perpendicular to the edge direction have drasticallychanged gray values. The nighttime road surface imageis affected by uneven illumination, so it is not suitable touse a special threshold segmentation algorithm to obtainbinary images. The edge detection algorithm can obtainbinary images without using a special threshold segmen-tation algorithm. The edge of the object is the regionwhere the local brightness of the image changes signifi-cantly. The identification and extraction of the imageedge is very important for the recognition and under-standing of the entire image scene. It is also an import-ant feature that the image segmentation depends on.The commonly used edge detection operator is theSobel operator, LOG operator, Kirsch operator, Cannyoperator, etc. [14]. Various algorithms have advantagesand disadvantages for different occasions. The purposeof this paper is to explore an algorithm that can detect

(a) (b)

(c) (d)Fig. 3 Comparison of test operator results. a Sobel operator. b LoG operator. c Kirsch operator. d Canny operator

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clear lane line edge images and quickly and efficientlyidentify images.Combined with the principle of each detection algorithm,

a comparative analysis is performed using the curve imageas an example and Matlab simulation is performed. Theresult chart is shown in Fig. 3.Through the comparison of the above groups of inspec-

tion charts, it is not difficult to find that the Kirsch detectionoperator has obvious advantages, the most abundant infor-mation extraction, and the best detection results. However,the Kirsch algorithm itself also has its own deficiencies, suchas large amount of computation, real-time, and poor valid-ity. For these problems, this paper will improve and upgradethe algorithm in the next section and use this algorithm todetect edge information in the entire system.

4.3 Lane line feature point recognitionBased on the road model recognition method, the straightline model is the simplest one. This paper mainly intro-duces the Hough transform to detect straight lines.The Hough transform [15] was proposed by Paul Hough

in 1962 and published as a patent in the USA. Its principleis to convert the detection problem from the image spaceto the parameter space and find the peak of the accumula-tor in the parameter space so as to achieve the targetdetection.The specific detection algorithm is as follows:In the image x − y coordinate space, the line passing

through point (xj, yj) is represented by:

y j ¼ uxj þ v ð2Þ

Among them, parameter u is the slope, and v is theintercept.If xj and yj are treated as constants and parameters, u

and v are treated as variables, then Eq. (2) can beexpressed as:

v ¼ −x juþ y j ð3Þ

The other point of (xk, yk) in the coordinate space alsohas a corresponding line in the parameter space:

v ¼ −xkuþ yk ð4Þ

This line intersects the line of point (xj, yj) in theparameter space at point (u0, v0) as shown in Fig. 4:It is known that when the slope of a straight line is

infinite or nearly infinite, it cannot be expressed inthe parameter space, so the polar coordinate systemis used next, and the polar coordinate expression isas follows:

ρ ¼ x � cosθþ y � sinθ ð5Þ

Where ρ is the vertical distance from the straight line tothe origin and θ is the angle from the x axis to the verticalline. The value range is ±π/2. When θ = 0∘, ρ is equal tothe intercept of the x axis. When ±π/2, ρ is the cutoff ofthe y axis distance.The point on the line in the x-y plane is mapped to the

parameter space as a sinusoid. As shown in Fig. 5c, theline equation can be determined by the intersection of thetwo sinusoids.In the actual calculation, the Hough transform builds

a two-dimensional accumulative array based on the par-ameter space by accumulating the curve values in theρ-θ parameter space [16]. Here, the quantization accur-acy of 1 or 0.5 is selected to quantize the variables ρ andθ as the two-dimensional array. The value of each cellrepresents the number of collinear points in the image.Therefore, the conversion of the straight line parameteris converted to the peak point in the search.The Hough transform detects lane lines with good

self-adaptability. When the lane lines are not continu-ous, it can still detect the effect very well and has goodrobustness.

(a) (b)Fig. 4 Hough transform in Cartesian coordinates. a Image coordinate space. b Parameter space

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5 Algorithm improvement andupgrading—discussion and resultsEdge is the most basic feature of the image. Due to gray-scale discontinuities and other reasons, differential opera-tors in these places are often relatively large. Therefore,when using such operators for detection, multi-directionaldifferential operators are generally used for processing. Theclassical Kirsch operator edge detection is to use eight dir-ection templates to detect the image and then select themaximum value as the image edge. However, the computa-tional complexity of processing images through eight detec-tion templates is rather large, and it cannot meet thereal-time and effectiveness of detection to some extent. Forcomputational problems, this paper improves and upgradesthe classical Kirsch operator, which effectively reduces theamount of computation [17].The Kirsch operator is a new edge detection algorithm

proposed by R. Kirsch. It uses a template in eight differentdirections to perform convolutional derivation of eachpixel in the target image, so as to obtain a complete imageedge information [14]. The specific principle is as follows:Assume that Mn(i, j) is the value at (i, j) in the nth fil-

ter template, I represents the input image, and I(x, y)represents the pixel value at the point (x, y) in theimage. First, design the eight-direction filter templateand use these eight templates to filter the target image[18, 19]. Set the filtering value at the point (x, y) ob-tained by n template filters to be fn(x, y). The value def-inition is as follows:

f n x; yð Þ ¼Xi; jð Þ∈σ

I xþ i; yþ jð ÞMn i: jð Þ ð6Þ

where σ represents the filter template 3 × 3 region,i = − 1,0,1; j = − 1,0,1; x = 1,2,...,H; y = 1,2,...W, H, and W re-spectively represents the height and width of the image I.The largest filter value is:

f max x; yð Þ ¼ max f 1 x; yð Þ; f 2 x; yð Þ;⋯; f 8 x; yð Þf g ð7Þ

Then the extreme point image Fmax can be expressedas follows:

Fmax ¼F 1; 1ð Þ ⋯ F 1;Wð Þ

⋮ ⋱ ⋮F H ; 1ð Þ ⋯ F H ;Wð Þ

0@

1A ð8Þ

Among them, F(x, y) represents the gray value at thefiltered point (x, y). The edge image E is obtained fromthe extreme point image Fmax and the preset thresholdT, defined as follows:

E ¼E 1; 1ð Þ ⋯ E 1;Wð Þ

⋮ ⋱ ⋮E H ; 1ð Þ ⋯ E H ;Wð Þ

0@

1A ð9Þ

where E(x, y) denotes the pixel value at (x, y) in image E,when F(x, y) > T, E(x, y) = 1, otherwise E(x, y) = 0.In the actual road image detection, because the lane

lines are only in four directions, the four templates areused to filter the target image. The specific template se-lection is as follows:

M1 ¼ 18

−2 −1 0−1 0 10 1 2

0@

1A M2 ¼ 1

8

2 1 01 0 −12 1 0

0@

1A

M3 ¼ 18

1 1 2−1 0 1−2 −1 0

0@

1A M4 ¼ 1

8

0 −1 −21 0 −12 1 0

0@

1A

ð10ÞThe Kirsch operator is composed of eight 3 × 3 win-

dow templates, each representing a specific detection[6]. As follows:

5−33

50−3

5−3−3

! 55−3

50−3

−3−3−3

! 555

−30−3

−3−3−3

! −355

−305

−3−3−3

! −3−35

−305

−3−35

! −3−3−3

−305

−355

! −3−3−3

−30−3

555

! −3−3−3

50−3

55−3

!

.

At this point, according to the classical algorithm, thenumber of addition operations is P = 7 times, the number

(a) (b) (c)Fig. 5 x-y space and corresponding parameter space in polar coordinate system. a x-y space. b k-b space. c -θ space

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of multiplication is M = 2 times, the number of addition ineach direction is 7 × 8 = 56 times, and the number ofmultiplication is 2 × 8 = 16 times. Therefore, for an N ×Mimage, the number of addition operations needed to im-prove the algorithm is P = 56N2 times, and the number ofmultiplication operations is M = 16N2 times.Suppose that the eight template factors are arranged

in a clockwise direction into matrix M, then M is a cyc-lic matrix, and the value of each row element is shiftedto get the value of the next line element [19–22]. Thetransformation of M is carried out that:

ð11ÞThen

MP ¼ 8A ð12ÞThrough (11) and (12) can get

B‐1CP ¼ 8A; CP ¼ 8AB ð13ÞExpand (13) and move by:

r0 ¼ 0:625 p0 þ p1 þ p2ð Þ−0:375 p3 þ p4 þ p5 þ p6 þ p7ð Þr1 ¼ r0 þ p7−p2

r2 ¼ r1 þ p6−p1

r3 ¼ r2 þ p5−p0

r4 ¼ r3 þ p4−p7

r5 ¼ r4 þ p3−p6

r6 ¼ r3 þ p2−p5

r7 ¼ r6 þ p1−p4ð14Þ

Calculate the number of operations required for (4)P = 7 + 2 × 7 = 21 times, the number of multiplicationoperations M = 2 times; so for an N ×M image, theimproved algorithm required to add the number ofoperations is P = 21N2 times, multiplication times forthe M = 3N2 times.It can be concluded that the algorithm has reduced

the amount of addition and multiplication operations,and it reduces the amount of calculation while im-proving the effectiveness and real-time performanceof the entire system. The improved effect of the algo-rithm is shown in Fig. 6:From the comparison chart, we can see that when the

computational complexity of the operator is greatly re-duced, it does not affect the detection effect and theedge detection is still good.

6 ConclusionsThe development of intelligent transportation haspromoted the research and development of lane linedetection. The intelligent development process is alsofaced with many difficulties and deficiencies. This art-icle is based on these difficulties and deficiencies, an-alyzes the causes of the line, and detects the edge ofthe lane line. The basic questions were systematicallystudied. Through the improved Hough transform andKirsch operator, the robustness and adaptability of thedetection results are enhanced, the redundant infor-mation of the operator is reduced, the computationalcomplexity of the algorithm is reduced by the matrixoperation, and the detection of lane lines is improved.Speed enhanced real-time. The development of theInternet of Things era will surely promote the devel-opment of smart vehicles to achieve new break-throughs and innovations. We expect smart cars toapproach the homes of ordinary people as soon aspossible, bringing convenience, safety, and comfort toour lives.

(a) (b)Fig. 6 Optimized Kirsch detection operator. a Before optimization. b Optimized

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AbbreviationsFPGA: Field Programmable Gate Array; MATLAB: Matrix laboratory; RGB: Red,green, and blue mode

Authors’ contributionsZW is the first author. He designed the study, performed the research. WW isthe communication author. She helped the first author and collected thedata. Both authors read and approved the final manuscript.

Authors’ informationZhongxun Wang, male, born in 1965, Ph.D, is a professor. His researchdirection is source channel coding in wireless communications.Wenqi Wang, born in 1992, is a master degree candidate of Yantai Universityand her research direction is mobile image processing and lane detection.

Ethics approval and consent to participateNot applicable.

Consent for publicationApproved.

Competing interestsThe authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in publishedmaps and institutional affiliations.

Received: 30 May 2018 Accepted: 28 August 2018

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