o to 80ppm range of turbidity.

Transcription

1 REAL TIME MONITORING OF TURBID WATER BY USING VIDEO CAMERA PERSONAL COMPUTER AND IMAGE PROCESSING ' Nobuyuki Mizutai.Kazuya Saito,Yoichi Numata ASIA AIR SURVEY CO.,LTD. 3-6 Tamura-cho,Atsugi-shi.Kaagawa-ke,243,Japa. Shigeru Kurosaki,Shigeru Sakama. Osamu Nakamura TOKYO ELECTRIC POWER COMPANY CO. LTD. -6 Uchisaiwai-cho,-Chome,Chiyoda-ku,Tok~o,,Japa. ABSTRACT Turbid water caused by egieerig workig, harbors works ad so forth sometimes damages to the fishery ad ihabitat i the water. To moitor ad to prevet water evirometal deterioratio, it is eeded ot oly to costruct the real time moitorig system but also to determie the stadard value of turbidity for assessmet of water eviromet. I this study, the real time moitorig system which cosists of video camera, persoal computer, image processig uit ad software has bee developed ad examied to cofirm the adaptability for moitorig the turbid water distributio. The results of examiatio show the potetiality of moitorig the turbid water distributio ad estimatig the value of turbidity. Key word: Video Camera, Remote Sesig, Image aalysis, Eviromet,.. INTRODUCTION I recet years attetio has bee focused o the global evirometal problems such as the gree house effect, acid rai, desertificatio, deforestratio, ad marie pollutio. O the other had, local evirometal problems such as oises, air pollutio, red tides, ad turbid water have also become to obstruct the comfortable huma lives. I this study, we have treated the turbid water which caused by the egieerig or costructio works for marie istitutios. sice the turbid water sometimes damages to the fishery ad ihabitat i the water. I the covetioal method to moitor the evirometal deterioratio of water we used to measure the water quality i' the fields from the ship ad/or aalyze the turbidity of water i the laboratory. The aother method is the remote sesig techique which gather the iformatio about the water surface from the space. This method is practically used for surveyig the water quality such as the red tide, oil pollutio, etc. ad is realized its usefuless. However these methods have some problems. I the former case, the observatios are doe at poits or alog the route, so areal iformatio that shows the extet ad patter of turbid water ca ot be got from this method. I the later case, the chaces of observatio are restricted with weather coditios such as cloud, fog, ad strog wid ad iterval of satellite observatio. We have developed a ew water quality moitorig system which cosists of color video camera, persoal computer ad image processig software, ad tested this system's utility for moitorig of turbid water distributio caused by harbor works at power plat. The result shows the potetiality of this system for moitorig the distributio of turbid water ad for estimatig the value of turbidity i real time with successively. The accuracy of estimated turbidity value is withi +/- 5ppm i the rage betwee ppm ad 4 ppm. The 4ppm is the upper limit value for self cotrol. 2.BASIC FUNCTIONS REQUIRED TO THE SYSTEM Two basic fuctios are eeded i this system. Those are as follows. 2. Aalog/Digital coversio of video images Image processig to extract the distributio of turbid water should be doe digitally i real time. So, the fuctio of A/D coversio of video data (iput to image processig system) i real time is required. 2.2 Coversio of digital image data to value of turbidity The degree of ifluece by turbid water should be assessed accordig to the value of turbidity. So, the fuctio of coversio of image data to value of turbidity is required. Further, the results of image processig should be displayed o CRT with pseudo color image havig iterval of ppm from o to 8ppm rage of turbidity. 3. Hardware 3.THE CONFIGURATION OF SYSTEM The cofiguratio of developed system is show i Figure-i. It cosists of color CCD video camera (SONY XC-7), image processig uit (IMC-58V8, iclude A/D coverter), persoal computer (NEC PC- 98) ad color temperature meter etc. All of these istrumets are off-theshelf. 52

2 ..; VTR Color Figure-l The cofiguratio of system. 3.2 Software The softwares for this system maily cosists of two parts as follows, ad are writte by C laguage ad macro assembler Iitialize The values eeded for correctio processig should be etered for iitializig the system. Based o these values, prelimiary correctio is determied by multiplyig shadig correctio by surface reflectio correctio. Regard to shadig correctio ad surface reflectio correctio refer the followig sectio Image processig I the image processig step, we make fial correctio at first from multiplyig prelimiary correctio by RGB's balace correctio s that calculated from color temperature meter. The usig bibad image calculated from video image data which after correctio ad the quatitative model which represets the relatio betwee value of image ad turbidity, we covert the image data to turbidity's value, ad fially display the result o the CRT. The details of this step are metioed ext sectio. The flow of image processig is show i Figure-2. 4.CORRECTION OF VIDEO IMAGE DATA The video images take i the field iclude may kid of oises which prevet the precise aalysis. I the followig, we show the correctio algorithm which developed for system to remove these oises ad to extract the turbid water distributio exactly from video images. 4. Correctio of shadig Shadig is the pheomeo that the brightess decrease accordig to the separatio from the ceter of image ad is ofte observed i the optical istrumets which use the les. I the video images, this shadig effect occurred ad brig about wrog data to the aalysis of turbidity. This shadig is iheret to the system icludig the les to be used ad sigal processig uit. The the correctio to elimiate shadig efrect is eeded for each system. I our system, the correctio of shadig for whole image area is calculated by takig ratio of each pixels value to the value of pixel positioed ceter of image. 4.2 Correctio of surface reflectio The developed system is desiged to moitor the object area from the lad. This meas that the object area is viewed obliquely by video camera. I this case, the reflected ray from the surface add to the ray reflected from the materials uder the surface which relate to the turbidity. This surface reflectio becomes to the oise ad its volume depeds o the relatio betwee icidet agle of ray ad viewig agle of video camera. As the surface reflectio is iflueced by may s such as wave ad billow caused by wid, distributio of sulight ad skylight ad etc., we adopt the empirical method as follows. The video image data at may depressio agle are collected sychroously with the vertical video image data for homogeeous water surface. Usig these data, the ratio of image to vertical image at each depressio agle are calculated. Figure-3 shows the procedure of takig oblique image ad vertical image for same target. Vertical,/,/ -I,...,QL rC;---<,CL B", ",- Oblique () : Depressio agle Sea surface Figure-2 The flow of image processig Figure-3 The procedure of takig oblique image ad vertical image for same target. 52

3 ~.,. 3:' Ad Figure-4 is the graphs which show the ratio betwee each depressio agle data ad vertical data of blue, gree ad red bad. I the graphs the regressio lies are plotted. To correct the surface reflectio oises i video image, we have used these formula ad correct o each pixel of whole image. Actually these correctio may be chaged accordig to the weather coditio, sea state, altitude ad azimuth of su, viewig agle of video camera ad so o, we must make the correctio for each case. But, it is ot practical to make the correctio for each case. Therefore, we make the regressio lie which possible to be used for all of data. The formula of each regressio lie are as follows. R G B Crr=-exp(-.46*D-.6) Crg=-exp(-.45*D-.5) Crb=.-exp(-.7*D-.5) (D: depressio agle;degree) Correctio Correctio Correctio o.9.8.~:: ~ "~' ;~.~ ' C c.~';~ Red M'-"~"-~- ~c u, c : ; Depressio BDBlp '.Q< ':9 :" Gree j~..., Depressio agle.e. C M.::....ll. :j:s6 '::,::: ~ 'l '= '" Blue Depressio agle Figure-4 The graphs which show the ratio betwee each depressio agle data ad vertical data of blue, gree ad red bad. 4.3 Correctio of RGB balace The video camera collects the iformatio with three bad, amely, blue, gree ad red. To express the color of object exactly as color temperature by video camera, it is ecessary to adjustig the balace betwee red, gree ad blue. This is called as "white balace" ad is essetial for extract physical characteristics of object from video images, such as quatilizatio of turbidity of water(expressed by reflectace) from the data obtaied by video camera. The output values of red, gree ad blue from video camera are writte as follows. ER' EG' EB' Kr * ER Kg * EG Kb * EB ER, EG ad EB are reflected eergies from the turbid water at wavelegth red, gree ad blue respectively, ad ER', EG' ad EB' are values of output sigals from video camera respectively. Kr, Kg ad Kb are white balace coefficiets of video camera determied by system's itesity. The ideal values of white balace coefficiets for the "white" is Kr=Kg=Kb=l. Though we ca adjust white balace at every color temperature with the method metioed above, we must take the data of white object at each time whe the color temperature chages to correct white balace. It is ot practical to do this process durig the field observatio because of icessat fluctuatio of color temperature of the object. O the other had, the video cameras made recetly have a mechaism of adjustig white balace. It uses the balacig of output from video camera about reflected eergy from object, amely. equalizig the value betwee ER-EG ad EB-EG. This method is ot i eed of takig a iformatio of target of white color, ad the correctio process ca be doe automatically. However, this method is liable to be affected by chages of color temperature, because the value of output sigals from video camera is ot related to color temperature liearly. Therefore, we adopted a ew method to the system. We determied at first experimetally the ratios of output sigals of red ad blue bads to output sigal of gree bad respectively for may color temperatures. The these relatios are etered i the system. Through these process, oce the color temperature at observatio time is etered to the system before the start of data gatherig, we ca extract color iformatio(physical characteristics such as reflectace from turbid water) based o the give stadard value. The relatio that etered ito the system is show i Figure-5. Correctio ; : t. t ~*~ ~~,:.~)... ~... ~... ~~~p::: ~:::a. R' +:qr,-,!- + o... ~~~~T~~ ±~~i~'~=-\~'~~'~'~~'~'~'=-';'" G' : mj~~e:- +.j::----:-:",:. : a.s.,," ~.USJE:P" ":."...:..."".:"...;'.::!t>.'"-j;.~_'!'...,,:.t. B. a.s C!-e. : ~ ~ l ~ l : :.~....,... a.7~4r ~--~----~-----t~----~ ex Ol ) Color temperature (OK) Figure-5 The relatio betwee color temperature ad correctio that eterd ito system. 522

4 4.4 Bi-bad calculatio ad quatitative aalysis It is ecessary to estimate the turbidity of water for assessig the ifluece of turbid water to eviromet. We have developed the quatilizatio model which uses the correlatio aalysis betwee video image data ad turbidity of water. To get a absolute value of turbidity, the field observatio usig turbidmeter was doe sychroously with the collectio of video image data. For correlatio aalysis, we use the bibad video data which ca represet more accurate turbidity of turbid water. The formula of quatilizatio model chages accordig to the compoet of soil icluded i water, amely color of soil. Therefore it is ecessary to examie the relatio betwee video image ad turbidity ad to make a quatilizatio model for each costructio site before startig the moitorig. 5.RESULTS AND DISCUSSION 5. The quatitative aalysis model We have made two kids of quatitative aalysis models i this study. Oe is the model for dark blow water which occurred by the soil of sea bottom at the harbor of power plat as a turbid materials, ad other is the model for yellow brow water which occurred by the soil at costructio site. Bi-bad video image which ormalize the chage of radiace are used as iput data for the model. Through the model which correlate the video image ad turbidity of water, we have derived the expoetial fuctios as a regressio lie for quatitative aalysis. The regressio lie for dark blow water at the harbor, of course, differed from the yellow brow water at costructio site as metioed before. Those are show i Figure-6, ad the 99% sigificace level of the regressio lies are show i Figure-7. It is realized that the accuracy of model is 4ppm's turbidity. This value satisfies the accuracy requested for the system Turbidi ty 3 : y=(. 746E-6).e~«(.678E+Ol)*X}R=.88 5~ ~---' ~ (R*B/G*G) (l)model for dark brow water. : y= (2. 32E-)*e~ «(3. 89E-2).X} R=O ~ ' Turbidi ty (R*R/G) (2)Model for yellow brow water. Figure-6 The expoetial fuctio of the regressio lie for quatitative aalysis. Sigificace level Predicted value G (l)model for dark brow water. Sigificace level I Predicted value (2)Model for yellow brow water. Figure-7 The 99% sigificace level of the regressio lies show i Figure Evaluatio of image data The video camera of the system was set up at the smokestack of the power plat, ad the images which show the distributio of turbid water caused by the taker i the harbor were take. These video images were aalyzed usig the method metioed before. Photo- is a video image prited through the video-priter, ad Photo-2 is the result of aalysis which shows the distributio patter of turbid water. This data was take with 8 degrees depressio agle, so the specular reflectio of the su was icluded i the image. Though the specular reflectio 523

5 ca't be corrected i this system, the result obtaied from our system shows the relatively good agreemet with the field observatio. For example, turbidity of 4-5ppm derived from the system correspod to 3ppm of field observatio. 7.CONCLUSION We have developed the simple system for moitorig the turbid water. It cosists of iexpesive color video camera, persoal computer ad etc. Though the utilizatio of this system is restricted with weather coditio, evirometal coditio ad etc., The images which show the distributio of turbid water ca be got about three miutes after the collectio of video image data. Further, oce the model which coverts the video image data to turbidity is costructed, we ca get the distributio of turbid water with required accuracy. The result obtaied through the experimet shows the possibility of this system for applicatio to moitorig the eviromet of costructio site ad so o. Photo-l The color video image. a.reference Audiovisual Cosultat Ceter, Video Commuicatio Magazie, AVCC, p5 988, p9- Group V, 988, Techical Had Book For Video, Audio Video Magazie Japa Remote Sesig Research istitutio, Image Processig ad Image Aalysis, p24-p47 Photo-2 The Image is result of aalysis. O the other case which uses the color of sea bottom soil(dark brow), we ca't get a good result because of the similarity with the color of sea surface ad turbid water. That is, we ca't apply the same model to differet case because of the differece of red, gree ad blue reflectace value at each turbid water. 6.RESTRICTION OF THE SYSTEM AND IM PROVEMENTS OF THE SYSTEM Syouji Hashimoto, 98, CCTV Desig Guide, Coroa System Yasuhiro Sugimoto etc, 985, Optics of Ocea Eviromet, Tokai Uv, p8-p6 The turbid water whose color is similar to the surroudig water ca't be extracted usig the developed system. But, for the other case, we ca get the turbidity withi the required accuracy, for example, to cotrol the turbid water of costructio field withi evirometal stadard value(e.g. 25ppm). The effect of su glitter ad the reflectio of breakwaters, groud, costructio machies ad etc. ca't be removed by the surface reflectio correctio, ad the parts of image where ships, foams, waves ad so o exist are ted to estimated relatively high or low value. The, we have to develop the method to discrimiate the materials which mislead the results from the turbid water. For practical use of this system, we must solve the problems metioed above ad must to costruct advaced system which automatically judge the level of turbidity ad war to maager about the coditio of turbid water distributio. 524