Research in Algorithm of Image Processing Used in Collision Avoidance Systems

Transcription

1 Sensors & Transducers, Vol 59, Issue, November 203, pp Sensors & Transducers 203 by IFSA Research n Algorthm of Image Processng Used n Collson Avodance Systems Hu Bn School of Cvl Engneerng and Transportaton, South Chna Unversty of Technology No 38 Wushan Road, Tanhe Dstrct, Guangzhou 50640, P R Chna Receved: 7 November 203 /Accepted: 20 November 203 /Publshed: 30 November 203 Abstract: Wth the rapd development of Intellgent Vehcle System (IVS) method, ts research nvolves dfferent knd of felds, such as computer measurement, control method, computer vson method, sensor data fuson method and vehcle project In order to control the vehcle automatcally only based on the mages, we use some specal technque of mage processng method to extract the feature of the mages, the process s complcated, t nvolves three steps The paper frst ntroduces mage processng method, the technology and ts applcaton, then the paper focus on collson avodance system to explan why mage processng s mportant to t What s more, the paper manly explan algorthm of mage processng, nclude mage enhancement, detecton algorthm of mage edge, fast nverse transform algorthm based on characterstc curve Fnally, the paper gves an example how these mage processng apples n road scene and ts effectveness Copyrght 203 IFSA Keywords: Collson avodance system, Dgtal mage, Image enhancement, CF-IHT algorthm Introducton The computer mage processng could convert an mage sgnal nto a dgtal sgnal to be processed by computer [] Snce computer processng s fast, and the dgtal sgnal have lttle dstorton, easy to store and transport wth strong ant-nterference ablty and other characterstcs, computer mage processng s wdely used n many major areas Dgtal mage tamper detecton s done by analyzng statstcal propertes of the mage contents of the authentcty of the dgtal mage, scene authentcty and ntegrty of authentcaton methods, namely whether the mage s the orgnal mage, the mage s real and whether they contan other secret nformaton mages, these technologes s the dgtal mage forenscs [2] The mage processng ncludes the followng aspects: frst, mage to dgtal converson and the assocated mage restoraton; second, geometrc transplant, such as translaton, rotaton; thrd, modelng; fourth, lne color control; ffth, the use of curves, and surfaces; sxth, the transformaton between color [3] Computer mage processng technology can be appled n the agro-processng, wth such technology t wll acheve the goal harvestng or processng of agrcultural products automaton and reducng manpower burden, for example, mushroom pckng automaton system and shrmp processng operaton control system Computer mage processng technology can also be appled n the ndustral producton automaton [4, 5] For example n the ndustral producton automaton, confguraton dagram of some machnes and related parts and the producton lne dentfcaton system devce use computer mage processng technques to dentfy the relevant system confguraton process, and transmt accurate control or the desred structure to the machne control system or robot, n order to acheve automaton of producton lnes [6] Computer mage processng technology can be appled n road traffc 330 Artcle number P_RP_000

2 Sensors & Transducers, Vol 59, Issue, November 203, pp whch s named as road traffc camera montorng system Computer mage processng technology can also be appled combnng wth remote sensng technology [7] Computer mage processng technology can also be used n engneerng desgn drawngs, nformaton converson process The common mage processng method s electrcal analog processng technology and Optcal - computer processng technology Electrcal analog processng technology could convert the lght ntensty sgnal nto an electrcal sgnal, and then use the electroncs to deal wth the sgnal such as add, subtract, multply, dvde, splt the concentraton, contrast amplfcaton, color synthess, spectroscopc contrast and so on [8-0] Image processng technques nclude mage compresson, mage enhancement and restoraton, mage matchng, descrpton and dentfcaton In order to mprove the pcture qualty, pcture mage enhancement s needed, such as ncreasng contrast degree, geometrc dstorton correcton and so on Image enhancement methods can be dvded as frequency doman method and the spatal doman method [] Low-pass flterng can remove nose n the mage; hgh-pass flterng can enhance the edges and other hgh-frequency sgnal, so the pcture becomes clear Image restoraton s a technology used when the mage s blur or wth nose, to estmate the orgnal mage [2] Image matchng, descrpton and recognton s the man purpose of mage processng, whch s to obtan an mage wth a clear sense of mages or graphc made up wth values or symbols nstead of document whch s randomly dstrbuted In ths paper, dgtal mage forenscs certfcaton of the authentcty of the natural scene mages and computer-generated mage detecton research was demonstrated 2 Collson Avodance System 2 The Conssts of Automotve Collson Avodance System Automotve collson avodance system s conssts of three parts The sgnal acquston system: use radar, laser, sonar and other technology to automatcally detect the vehcle speed, speed of vehcle before and the dstance between two vehcles Data processng systems: computer chps processed the nformaton for two cars nstantaneous dstance and relatve speed of the two vehcles to determne a safe dstance between the two vehcles, f the dstance s less than safe dstance, data processng system wll ssue commands Implementng agency: responsble for conductng the command from the data processng system, make an alarm to alert the drver brakes, f the drver s not executng nstructons, the mplementng agences wll take measures, such as closng wndows, adjust the seat poston, lock the steerng wheel, automatc brakes 22 Spatal Doman Processng In mage feature extracton technology, the edge nformaton s a very mportant feature Image sharpenng technology s a very effectve mage edge enhancement technology The mage s the hgh frequency part of the mage In the spatal doman processng, mage sharpenng technology s the same way as mage smoothng technology, to convoluton of mpulse response of mage and the flter In the ntellgent vehcle system, preventng vehcle collsons from obstacles on the road s an mportant factor to ensure traffc safety, collson avodance systems n ntellgent vehcle system s of great mportance When we drove, we receved nformaton are almost all from the vson When we drove, we receved nformaton are almost all from the vson Traffc sgnals, traffc patterns, road sgns and so on can be seen as a vsual communcaton language between envronment and the drver Computer vson manly plays the role of envronmental detecton and dentfcaton n ntellgent vehcle collson avodance system studes Compared wth other sensors, machne vson has advantages lke large amount of detected nformaton and beng able to detect nformaton far away, ts dsadvantage s that when n a complex envronment, extractng the target and background detected, requres a large amount of computaton If you just want to solve the problem under the current hardware condtons, t s easy to lead to poor real-tme system In ntellgent vehcle collson avodance system, the computer vson plays a very mportant role, t s one type of the most mportant sensors It s the mage processng technology that enables operaton of ths sensor, f there s no effectve mage processng technques, the automatc control of the whole system wll fal, not even acheve the purpose of automatc avodng vehcle crash 3 Algorthm of Image Processng 3 Natural Image Formaton Mechansm A dgtal camera was a fashon dgtal product, wth ts powerful functon, easy operaton and excellent shootng, by the majorty of consumers CFA color flter array s determned by the color flter array s nlad, each pxel locaton n one color only allowed through, blockng the other two frequences of color through The color flter array of the CFA was shown n Fg Applcaton of the CFA color mage sensor array, must apply to the nterpolaton pxel value obtaned obtaned n the three prmary colors at each pxel locaton nformaton, the resultng color values are generally also for color correcton and whte balance operatons In order to enhance the vsual effect of the dgtal mage, the mage sensor must also be a lnear response, and the core flter to be adjusted, the 33

3 Sensors & Transducers, Vol 59, Issue, November 203, pp mage processng further comprses an addtonal gamma correcton Fg 3 Contrast of gray values 33 Frequency Doman Processng Fg The color flter array of the CFA 32 Gray Value Trmmng of Image Pxel Gray value trmmng of the mage s a smple and effectve enhancement operaton It s manly n two forms: one s the gray value correcton, to modfy the ndvdual mage pxel gray level to compensate for the uneven exposure of the orgnal recorded mage; another form s gray value mappng transformaton, whch ams to use unform approach to change the whole mage grayscale or change some gray areas of the mage n order to ncrease the contrast degree, makng mage detals more vsble If set the orgnal gray doman of f(x, y) s [a, b], after lner transformng, the gray doman of f' (x, y) s [a', b' ], the relatonshp between f(x, y) and f' (x, y) s: f' (x, y) = a'+(b'-a')/(b - a)(f(x, y) - a) () Fg 2 and Fg 3 show an example of a standard mage of rce and mage whose contrast degree enhanced through the gradaton value lnear transformaton From subjectve evaluaton and hstogram, t can be seen n after transformaton the contrast of gray values ncrease, thus emphaszng the detals of the orgnal mage to get better edge detecton results Sgnal, as a two-dmensonal mage, can be mapped to the transform doman as same as the onedmensonal sgnal by the Fourer transform method, n whch the transform coeffcents reflects the characterstcs of the mage Frequency doman processng s a technque to modfy each frequency band of Fourer spectrum of the mage at dfferent degree, based on certan mage model Structure dagram of frequency doman processng system was show n Fg 4 Fg 4 Structure dagram of mage processng system Intrnsc propertes of transform doman s often used n mage enhancement, for example, make a low pass flter to let low frequency components pass and effectvely prevent hgh frequency components, whch means flterng nose of hgh frequency n the frequency doman, and then through nverse transform you can get smoother mages 34 Detecton Algorthm of Image Edge Fg 2 The orgnal mage of rce and mage after transformaton The mage edge s a collecton of pxels whose surroundng pxel wth a step change or roof n gray varaton Edge exsts wdely between object and background between objects One s step edge, between adjacent areas of two dfferent gray values n the mage Another edge s the roof-shaped, whch s located n turnng pont of gray value For the step edge, the second dervatve at the edges s zero crossng; for the roof-lke edge, the second dervatve at the edges s of the extreme value Dervatve operators could emphass on gray change, f 332

4 Sensors & Transducers, Vol 59, Issue, November 203, pp dervatve operator s used, the value s hgher at pxel wth large gray changes So you can set the dervatve values as edge ntensty of the correspondng pxels, by settng the threshold value to extract the edge pxels The paper uses LOG operator, t s: g= [h(x, y)*f(x, y)]=((r -σ )/σ ) exp(-r/2σ )*f(x, y) (2) Through the nature when second-order dervatve operator s at zero pont, t wll determne the locatons of the mage ladder-lke edge The edge detecton algorthm of Log operator s as follows: Obtan fltered mage wth Laplacan-Gauss flter for mage flterng Do zero-crossng detecton for the mage obtaned: assume that the pxel of frst-order dfferental mage s P(, j), L(, j) s ts Laplace value Determne n accordance wth the followng rules: f L(, j) = 0, we could conclude whether (L( -, j),( +, j)) or (L(, j-), (, j+ )) contans two number wth opposte sgn As long as one of these two number couples contans two numbers wth the opposte sgn, then P(, j) cross zero And f the frst-order dfference value of P(, j) s greater than a certan threshold, P(, j) s an edge pont, otherwse not If L(,j) 0, then determne four number couples (L(, j),(-,j)), (L(, j),(+,j))(l(, j),(,j-)), (L(, j),(,j+)) (3) contans two numbers wth opposte sgn, f do, pont besdes P(, j) cross zero, and f the frst-order dfference value of P(, j) s greater than a certan threshold, P(, j) s an edge pont, otherwse not operator edge detecton algorthm n subsequent experments as the mage edge detecton method The detecton algorthm of Log operator was shown n Fg 5 35 Fast nverse Hough Transform Algorthm Based on Characterstc Curve Boundary trackng, also known as the edge pont lnkng, search from an edge pont n edge gradent map to the adjacent edge ponts so as to gradually approach the detected boundary Follow the prncple of the boundary trackng to look for "growth pont", based on some characterstc curves detected, such as the slope of the lne, the orgn of the crcle, the radus, the curve equaton and so on, accordng to the Hough nverse transform reconstructon crtera to determne whether the reconstruct these "growng pont" If they meet the condtons for reconstructon, then ths "growth ponts" would be reconstructed Then set these "growth ponts" as "feature ponts" for a new round of the reconstructon to fnd new "growth ponts", untl t reaches the pre-establshed testng standards (a) Intalze the reconstructed mage A -nv :set all elements of matrx A - nv(m N) as 0 (b) Choose feature pont: the number of feature pont of orgnal mage s n (c) Reconstruct feature pont: set feature pont (x, y) and A(x, y ) = 0, then set nverse transform as left part and rght part, then calculate as follows: If, then x > y θ = tan - (y /x ) (3) θ = θ*sθ (4) θ = round(θ ) (5) ρ = (x cos(θ ) + y sn(θ ) )*sρ (6) ρ = round((x cos(θ ) + y sn(θ ) )*sρ (7) - = sρ*cos ( ρ - 05)/ ρ ) (8) θ Fg 5 The detecton algorthm of Log operator Do experment for log operator n the case wthout nose polluton, wth Gaussan nose, wth pepper nose, wth multplcatve nose, the Log operator mage edge detecton has good detecton results n the above three nose stuaton Therefore, we use log = floor( θ - (θ - θ)) (9) θ Check the pont C( θ -, θl ρ) of Hough parameter space defnton matrx C(θ( ρ), f the value s not zero, that means the curve where the pxel (x, y) s Hough transform makes a contrbuton to cumulate C(θ( ρ), the value of 333

5 Sensors & Transducers, Vol 59, Issue, November 203, pp (x, y) s, then set the value of pont correspondng to (x, y) as n matrx A -nv A - nv(x, y ) (0) C( θ - θ, ρ ) C( θ - θ, l l ρ ) - () x < y, (x, y) If belongs to rght part, the calculaton s smlar to left part, but the only dfference s : θ = floor((θ + θ) - θ ) (2) r Smlarly, check C( θ, + θ r ρ) n C(θ( ρ), reconstruct the pont y-a as the above rules Search for growth pont: accordng to boundary trackng prncple and nformaton of detected curve, search from feature pont, and search growth pont from these ponts Reconstruct growth pont: do as step 3, and store the results n reconstructed matrx, A -nv Repeat search wth set the growth pont n step 5 as feature pont Repeat step 3 to step 6, untl t reaches the pre-establshed testng standards Fnsh recyclng Software of computer mage generaton exstng can do and natural mages are very smlar n content, t s n one order and two order statstcal feature matchng, but n the detals of edge three moments lke that and four order moments are hard to match The flowchart of CF-IHT algorthm was shown n Fg 6 Consder from the pont of vew of mathematcs, the matchng of one order and two order statstcs are relatvely smple, but to acheve three orders or hgher order accurate matchng s a very dffcult task The flowchart of algorthm s: accordng to the method of boundary tracng followng a certan drecton to search for growth ponts, whch greatly reduces number of detected ponts, reduce those redundant pont unrelated to the detected curve, thus reducng the storage space In addton, the algorthm repeatedly usng the "loop", so that the parameter n the" loop" could reuse ther storage space, whch means, to some extent, t could solve the problem that the storage space requred by the algorthm SIHT s too large The loop of algorthm also reduces the computatonal complexty, each pxel could be processed n ths loop processng, so that the algorthm structure s smple 4 Smulaton of Road Scene 4 Image Enhancement of Road Scene The result of the experment shows that, f mage has been enhanced, the dfference of gray value of the edge of mage wth other ponts has been enlarged, when use Log operator to extract mage edge, the extracton result s better wth mage enhancement, the pont msjudgment has reduced The result of mage enhancement was shown n Fg 7 (a) The orgnal mage (b) The gray scale mage Fg 7 The result of mage enhancement Fg 6 Flowchart of CF-IHT algorthm CF-IHT would select n feature ponts from the M N ponts, then set these feature ponts as startng ponts, use some pror nformaton of the curve, 42 Image Feature Curve Extracton of Road Scene Do experment for the orgnal mage wth Gaussan nose, wth pepper nose, wth 334

6 Sensors & Transducers, Vol 59, Issue, November 203, pp multplcatve nose, and check f the Log operator mage edge detecton has good detecton results n the above three nose stuaton Add pepper nose wth densty 002 to check the results Salt and pepper nose generated polluton for mage whch s very smlar for ran and snow weather condton, therefore, the ablty to adapt to ths knd of nose s very mportant Image feature curve extracton wth dfferent nose was shown n Fg 8 extracton of the characterstc curve of the road scene, road lne nformaton of the mage can be obtaned, to realze ntellgent control of the vehcle Fg 9 The example of corroson operaton (a) The mage wth pepper nose (b) The detecton result of Log operator Fg 8 Image feature curve extracton wth dfferent nose Wth the above mages, the concluson can be drawn: Log Operators mage stll has good nose mmunty for a specfc scene, the edge detecton effect s stable, lttle affected by nose Therefore, we use Log Operators mage edge detecton, to provde bnary edge mage for subsequent experments 43 Image Edge Detecton of Road Scene In the mage acquston and transmsson process, Gaussan nose s most lkely to happen, so we chose an mage affected by Gaussan nose do feature extracton experments The example of corroson operaton was shown n Fg 9 In the experment, we frst through vsual sensor to catch an mage affected by Gaussan nose, do mage enhancement to the mage, then we use the Log Operators to make mage edge detecton, fnally use CF-IHT algorthm to extract characterstc curve n bnary edge mage The expermental results show that wth mage enhancement, edge detecton and 4 Concluson The paper s manly about mage processng technology, whose am s to fnd a sutable feature extracton algorthm to extract some nformaton form mage to support the realzaton of computer vson collson avodance systems Before mage feature extracton, mage must be dsturbed durng mage collecton and transmsson Based on mage enhancement, we then conducted mage feature extracton The paper selected the Log operator edge detecton algorthm to do edge detecton for the orgnal mage The paper then use CF-IHT algorthm to conduct mage feature curve extracton, whch greatly reduces number of detected ponts Fnally, we conducted smulaton experments for road scene mage, the results showed that, Log operator stll has good nose mmunty, then we got the bnary edge mage after feature curve extracton used CF-IHT algorthm to detect road lne curve, n order to provde effectve control nformaton for computer, and ultmately to realze the mage feature extracton purposes References [] Davs L S, A survey of edge detecton technques, CGIP, Vol 4, pp , 975 [2] Robert L G, Machne percepton of threedmensonal solds, Optcal and Electro-Optcal Informaton Processng, 965, pp [3] He Y, Zhang Y J, An analytcal study of the effect of nose on the performance of dfferental edge operators, n Proceedngs of the 2 nd ICSP, Vol 2, 993, pp [4] Wlknson M H F, Optmzng edge detectors for robust automatc threshold selecton: copng wth edge curvature and nose, GMIP, Vol 60, No 4, 998, pp [5] Marr Dю, Hldreth E, Theory of edge detecton, n Proceedngs of the R Soc London, Vol 207, 980, pp

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