CA2085124A1 - Automatic carcass grading apparatus and method - Google Patents

Abstract

Apparatus for the grading carcasses after slaughter comprises a plurality of video cameras (C1, C2) adapted to be positioned adjacent to a slaughter line (R) to expose an image of a carcass (P) on said slaughter line from a plurality of different viewpoints, signal processing means to derive from said images a plurality of parameters characteristic of said carcass, storage means to store a corresponding plurality of parameters derived from prior measurement of reference carcasses, comparator means to compare said plurality of parameters with said corresponding plurality of parameters to derive a further parameter indicative of the grade of said carcass and indicator means to provide an indication of the magnitude of said further parameter.

Description

WO 92/00~23 PCI-/GB91/01018 20~12~

Au~o~atlc carcass grading apparatus and ~ethod This invention relates to methods of and apparatus for the automatic grading of carcasses In abattoirs, processing plants 5 and the like. Hitherto, grading of carcasses has been performed manually and thus has been subject to variations between and within operators. The assessments made from such a manual operation are totally subject~ve. ln the case of beef carcasses there is an additlonal problem of perspective, the hind port~on 10 often belng several metres away from and above the grader. With the intention of el~minating operator variabil1ty, an automatic inspection system us~ng video cameras and itrtage analysis has been devlsed.
Carcasses are graded according to officlally accepted 15 criterla, which vary from country to country. A transparent system has been devised on wh~ch it is possible to superlmpose a variety of grading strategies. Examlnation of the total carcass as proposed in this method, gives information on speclfic sex, dlstrlbution and other attributes ~o wh~ch weighting factors 20 ~such as carcass weight and size) can be applied, ~f necessary, to determine an overall grade. Meat yield is calculated on the basis of normalised measurements, that ls, by taking one or more views of the carcass and shrinking or expanding the image to a standard size and then compar~ng it with historic data from 25 previous carcass measurements for which the meat yield and nteat yield distributlon has been determined. Initially this informat~on may be less preclse than required, but as the database on which such predictions are based expands, the precision of yield predictions and the accuracy of yield 30 distrlbution will steadily improve. This enhancement may progress e~ther in a passive manner by updates to the database or, in an actlve way, by means of an intelligent, dynamic database whtch continually expands through analysis of lts acquired data.
Interpretation of the measurements has been based on an .
. ' ~ ' '~ . , ,~ . ~ . , - , :
,: . : . . , . , . , ., . .
.
.

2n8S~2~

in-depth observation of carcass dressing operations. For instance, there are three ways of taking the hide off an animal - totally manual, semi automatic or fully automatic. A11 of these operate in slightly different ways, with the manual 5 technique belng the most variable of the three. Th~s means that the method of hide removal wlll determlne how much the fat and thin muscles of the belly reg~on ~around the cutanus ~runc~i) is actually ripped off. This, in turn, may indicate that the area around the belly is fat-reduced or even fat-free but 1t is a 10 totally false indication because the fat has actually been removed. System intelligence has been developed to account for this and other aberrations that can be caused by dressing practice.
European Patent Application No.0321981Al discloses a method 15 and apparatus for determinatlon of the conformation, fatness and other properties of individual cattle classes. The silhouette of a carcass or half carcass is recorded wlth a video camera ~n a special light screening chamber and a calculatlon of the parameters of the carcass made on the basis of an algorithm 20 derived using a number of subjective assessments made by manual carcass graders. A second image may be recorded from the same viewpoint using different illuminat10n.
One of the problems associated with this system is that it requires every carcass to go ~nto a special viewing chamber. It 25 needs a special loading and unloading facility and is therefore very limited in the number of carcasses that it can deal with.
In particular, it ~s restricted to slow slaughter lines, and, in practice would not be usable for animals other than cattle.
Furthermore, it requires modification of a slaughter line to 30 enable it to be taken into use. It is restricted to measurements taken from a single viewing point, and therefore cannot cope with a widely varying population. It is apposlte that the variability of beef carcasses in Denmark ~s very small indeed. It comprises almost a single line or a single cross and 35 about 85~/~ of it is young bull-beef, produced for the Italian .. . . . .
., . `

WO 92/00523 PCI/GB91tO1018 20~5124 market.
According to the present invention there is provlded a method of grading carcasses after slaughter comprising the steps of checking for the presence of a carcass in the field of view 5 of a camera, checking that the orlentat70n ol said carcass with respect to the camera is in accordance w~th a predetermined arrangement, exposing an l~age of the carcass from a plurality of different viewpoints , determin~ng a plurality of dimenslons of sa~d carcass from said 1mages and comparlng said dimensions 10 with stored values to determine the overall grading of said carcass.
There is also provided apparatus for the gradlng carcasses after slaughter comprising a plural~ty of video cameras adapted to be positioned adJacent to a slaughter l~ne to expose an image 15 of a carcass on said slaughter line from a plurality of different v1ewpo~nts, signal processing means to derive from said images a plurality o~ parameters characteristic of satd carcass, storage means to store a correspond~ng plural~ty of parameters derived from prior measurement of reference 20 carcasses, comparator means to compare said plurality of parameters wlth said corresponding plurality of parameters to derive a further parameter indicative of the grade of said carcass and indicator means to provide an indication of the magn~tude of said further parameter.
The invention will now be particularly described with reference to the accompanying draw~ngs and photographs, in which:
Figure 1 is a lateral view of a well formed carcass, side F~gure 2 ~s a dorsal v~ew of the same carcass half, Flgure 3 ls the posterior/anterior view of the same carcass, Figure 4 is a lateral view of a less well developed less well fleshed carcass, Figure 5 the dorsal view of that same carcass, Fi~ure 6 is the posterior anterior view of that less well fleshed carcass and . . ~ ; , : .
. . . ,. :

. . .

WO 92/0û~;23 PCI/GB91/01018 2o~5t24 Figures 7a and 7b are a schematic plan and side view of inspection apparatus set up in an abattoir.
Referring now to Figures 7a and 7b of the drawings, carcasses proceed along a rail R past a viewing position P. At 5 the viewlng position are three cameras Cl,C2,C.I . One camera is positioned above the rail to provide a posterior/anterior view of the carcass. The other two are positioned laterally so that their l~nes of view are at 90 to one another and 45 to the rail. It is therefore possible to obta~n lateral, dorsal and 10 posteriorlanterior views simultaneous1y. The same ob~ect~ve may be achieved by positioning the cameras at a 90 bend in the rail. In this case the cameras are again positioned above the rail and laterally at 90 to one another, but in this instance it is not necessary to orientate the carcass at 45 to the 15 rail. A third option is to have the cameras staggered linearly. Such an arrangement would require the carcass to be rotated through 90 after leaving the lateral viewing position ~C2) and before entering the dorsal v1ewing position (C3). The posterior/anterior camera (Cl~ can be suitably positioned above 20 the dorsal viewing position.
It would be possible with the second arrangement, to eliminate one lateral camera if ~t were posit~oned at the bend, but care would have to be taken to ensure that ~mages of other carcasses do not enter the field of view. In order to maintain 25 line speed, it may be prudent to introduce a preview camera C4.
Thls will enable the system to ensure that the carcass is correctly aligned and orientated prior to entering the grading station. If not, suitable remedial action to reposition the carcass can be taken without affectiny the the continuity of the 30 automatic grading operation.
With all animals, carcasses or split sides are supported by means of suspension from a hook or hooks running above a Fixed rail. Wlthin any one factory, the des~gn o~ that hook will be speciflc as their rail will only accept one type of hook, the 35 system being able to compensate for all variations in hook .... . . . .
!; . .

WO 92/00523 PCr/GB91/01018 20~5124 length.
The carcasses are viewed when in a (1) warm, (2) semi-warm or (3) cold state; the preferred states being (2) or (3) which occur some thirty minutes onwards after slaughter. Prior to 5 that the bulk of the fat ls translucent and any fat cover and distribution information must be interpreted in a completely dlfferent way because as the fat becomes cold ~t becomes more reflective and less absorbent to light, this providing greater accuracy on relatlve thickness data.
The temperature is not usually measured because the conditions within the slaughterhouse and the cooling rooms are generally fairly well controlled. Each system will be calibrated to a defined set of environmental charaeteristics specific to that plant or abattoir. If conditions change 15 radically, a new set of parameters will have to be installed.
Other information utilised in the grade calculations but not provided directly by the system are cold carcass weight or dressed carcass weight, that is when the hide and innards are removed and any miscellaneous pieces of fat such as channel fat 20 and kidney knob fat are taken out. Such data are readily available in most establishments via an automatic weighing system.
As the carcass progresses through the system, it is first correctly ori0ntated wlth respect to the predetermined position 25 of the cameras. If the orientation is not correct, but line speed and operation maintalned, then computer transformations and re-drawing would have to take place. This would require a lot of computing power and take a considerable time. Such a technique is possible on a beef grading line due to slower ltne 30 speeds. ~ith present technology, it i 5 impractical for pig, lamb and all white meat lines.
The sequence is predetermined by the way the views are to be interpreted. Preferably, three views are taken, although two of these can actually be generated from the data of the other 35 five. Provision has been made for another camera (C5) to .

'' ' ' ' .~ '' ' ~ ,' ' ' ',,'~' ' ' ' ' ' "'' "

, , ~ ' ~ . ' ~ "' ' , W O 92/005~3 PCT/GB91/~1018 2085~ 2~

provide data on the content and distribution of fat overlying the ribs inside the dressed body cavity from a medial carcass view. Data provided from such a view will only be required lf fat distribution data provided by the other defined camera 5 positions is unclear, ambiguous or insuffic~ent for grading to be accomplished. It is possible to magnify and zoom in on a particular image. Although the saving in equlpment costs is marginal, it does reduce the processing time, which on fast l~ne throughputs can be a signlficant advantage, although it may be lO accompanied by a marginal decrease in accuracy of yleld prediction.
Three general vlews of the carcass are taken to prov~de basic data on overall carcass characteristics such as length, extent of minimum and maximum width, area distribution curves, 15 etc. Each of the views are taken with a standard, non-interlaced high def~nition scan rate of l/50th second, up to 800 lines, although faster shutter speeds can be accommodated if necessary. For most applications alternate line sampling is sufficlent and permlts the picture information to be processed 20 much faster.
Three overall views provide the general carcass information. The information in these views is then processed by hardware, with a range of separation capabilities such as a differentiating clrcuit as an edge detector, or by dynamic 25 software that shifts thresholds according to the type of carcass being viewed.
~ ith certain species of animal, useful information can be obtained from an interior view of the carcass. However, due to si2e variations, it is advantageous to have a dynamic viewing 30 window9 the size of which is set in accordance wlth the dimensions of the carcass which have been determined prevlously.
Very thick fat exhibits a high reflectance value. ~ith sheep in particular there are some problems wlth the translucent nature of the fat. ~ith normal direct lighting, because at 35 specific carcass locations there is a film of tissue, and as .

WO 92/00!;23 PCI/GB91/01018 208512~

there is structure underneath it, although one is on top of the other, lt acts on occasions like a mirror, particularly when the carcass is st~ll warm. When it is at a particular angle it bends the light causing it to reflect along this collagenous 5 material, glv~ng rise to reduced contrast. The problem can be overcome by uslng d~ffuse or indirect llght.
In making yield predictions, lt is important to determine the sex of the animal, because a helfer of identical conformation and weight to a steer wlll have more saieable meat 10 on it than the steer just because the bone structure is lighter. Neck muscle is very pronounced and developed in a bull; the fat around udder region is smooth in cows and helfers but rough in bulls and steers. This and other d~stingulshing sex characteristics are determined by this system.
Image processlng for carcass evaluat~on is a computer-intensive operation because the outline curves involved are complex spline curves, that is, they are composite curves made up of a large number of elementary components. The angularlty, degree of curvature, length and other mathematical 20 and geometric descriptions at a specif~c point or region on the carcass can be used objectively to define a shape at that point ~~ ~ compariscin ~n the grading procedure and quantitatively to ct muscularity and thus lean meat yield.
mage analysis may be used in conjun~~ion with other 2S t Iniques and devices such as multiple w iength infra-red measurement to measure or predlct moisture content and so to predict the chemical liquid content.
~ hen the carcass is fresh, the water content can be predicted with some accuracy; with butchered meat, however, the 30 water status needs to be determined. Certain surface cuts of meat will lose water rapidly and lnterior cuts will lose water very slowly. The water content will effect the apparent lipid content. ~ith on-line production techniques, the ability to measure fat and water content will enable accurate predictions 35 of lean content to be made. This is necessary, for examp1e, in . ' ,.' ' ', ' ' :, .. . . . : :

. .~ . .

2 ~ 8 5 ~ 2 ~ - 8 -the manufacture of a calorie-controlled product.
With pig grading it is possible to obtain information on fat depth much more easily with other technologies rather than by image analysis. There are practical difficulties in looking at 5 the cut surface of a pig, arislng from factors such as blood splatter and fat smear. However, electrical resistance and reflectance probes are unable to measure chan~es in confcrmation and therefore t~nd to underestimate the lean content of well conformed animals and over-estimate saleable meat yleld in 10 carcasses of poorer conformation. Also, because their pigs are much more variable, European predictlon equations are not suitable for the American pig population. In such applications, the use of image analysis to pre~ict shape and muscularity, together with other technologies for fat depth informatlon, will 15 provide more accurate predictions of percentage yield. For beef, image analysis is well developed as a standalone technique. For sheep, because there are different fat distributions, particularly inter-muscular fat, other techniques, such as ultrasound scanning can provide valuable 20 supplementary information for both grading and yield prediction.
With poultry, fat content and distribution around the hind quarters of the animal, particularly the cloacal reg~on or around the neck fat are good indicators. These display themselves as changes in the carcass conformation. Therefore, 25 with appropriate adaptation the techniques described above can be utilised to grade poultry carcasses w~th or without the addition of techniques to quantify fat content at the positions defined.
There are a number of objectives with this image analysis 30 system. These include the ability to grade carcasses, which is important for producer payment, and predict yield, not solely for the total carcass but for some specific parts, such as the primals. The whole system not only has an attraction to the abattoir producer or the legislature, but it becomes a large 35 marketing tool for meat purchasers such as the major '. ' . . .

:' 3 PCr/GB91/0111~$ : ~
.
20~124 :

supermarkets, who at present de~ermine, from data at point of sale, the quantity and type of meat being sold, but can only subjectively determine wholesale meat purchases. This techni~ue will enable them to make bulk meat purchases based on accurate 5 assessments of saleable meat. In addition, carcasses may be bought on a quant~tative prediction of hindquarter to forequarter meat distrlbution.
In order to achieve an accurate prediction of primal y~eld, a cutting grid based on primitive measurements of length and 10 width may be drawn and superimposed on each of the basic two-dimensional views. Since butchering methods vary from country to country, the advantage of this system is a new cutting grid can be superimposed to mirror the changes in butchery techniques.
The v~deo cameras are specially constructed to achieve a predetermined spectral response. This may be ~odified, according to the specific circumstances, by use of filters, structured lighting, or by selectton of a charge-coupled image sensing device with the desired response. In some instances it 20 is not desirable to have filters in front of the cameras because it degrades the quallty of the image. The spectral response is chosen to increase and enhance the separation between the colour of the components of the meat, particularly the fatness. For example, in applications where the meat may be different shades 25 of red, purple or pink or the fat may be different shades of whlte or yellow, selection of the green output (from an imaging device with separate RGB output) gives the best separation for the lean meat colour in beef. Any one of the other RGB outputs may be used for the fat because the fat is reflecting across the 30 whole spectral wavelength range. Alternatively, for certain applications data can be taken from each of the RGB outputs and a polygonal database can be built up together wlth or in addition to luminance/chrominance data on the way in which the colours are changing relative to compositional variations in 35 component materials or alterations in physical or chemical . . . . : .

.

WO 92/00~23 PCI'/G~91/0101~
~ S~

parameters during production. This i5 valuable for process control operations. Particular products can be examined a specific points in its process and from a database knowledge of the components at those points may be interpreted to show that 5 there is, for example, too much fat in the product, or too much liquor, the pastry is not the correct cons~stency and so on.
This information may be transmitted backwarcls and forwards down a process line to modify the product in accordance with an ~deal market requirement. For most applications, however, lt is 10 sufficient just to keep to luminance, grey scale, or composite video information.
Because the efficiency of the system is influenced by a number of parameters including the luminance of the llghts, the reflectivity of each carcass, electronic drift in the cameras or 15 the system hardware, it is necessary for the system to be dynamically self-calibrating on system startup and self-compensating dur7ng operation. Self-compensation during operation is achieved by a combination of camera auto-iris, signal auto-gain and dynamic modification to system parameters 20 via the system software.
The system described herein is also capable of self-~iagnostics by means of which it is continually monitoring the performance of both the hardware and the software and will visually and audibly warn the operator of failures/errors and 25 their degree of severity.
~ ith such a proliferation of shape and tissue distribution information available from a number of two-dimensional views, such a system i5 capable of generating three~dimensional projections for individual carcasses. Using a process of 30 normalisation it is thus possible to create and modify cutting pathways for robotic and automated systems from known and defined shapes present in the database based on the measurements quantified by the image analysis system. A similar approach can also be applied to carcass dressing procedures.
By the inclusion of an intelliyent knowledge-based database, ,, . --20~12~

the system described above is capable of objective meat inspection and other allied tasks.
In summary, image analysis is used to compile carcass data based on historic experlence of what factors contribute to S yield. A multiple thresholding techn~que enables fat distribut~on and content to be derived. Complex shape information can be stored as simple elements of spline curves.
Other useful data, including carcass weight and specimen sex, can be used to bias the data as necessary. For particular 10 curves and shapes tolerance databases can be set up. This shape information will be a mixture of area measurement, connectivity analysis, length, widths, boundary points and edge detectlon.
Predetermined dlvisions can be set for each of these measurements and a bias introduced to those measurements to 15 enable accurate grade and precise yield lnformat'ion to be generated. In conjunction with other information, accurate grading and yield pred~ction can be made for the three major red meat species. With some modification this can be extended to poultry.
This system also provides the basis for rapid automation of butchery and dressing techniques to be developed. The addition of artificial intelligence extends system use into areas of carcass welfare such as meat inspection.
A flow diagram for a preferred method of carcass grading is 25 shown in Table 1.

. ' . - ' : :. : ~ : ,. . .

WO 92/ûO~Z3 PCI-/GB91/01018 . .

2 0 ~ S ~ 2 4 - 12 -Table 1 1. Check calibration 2. Carcass present/absent 5 3. Check carcass orientation 4. Measure overal1 dimensions for each v~ew (fat areas/bulk) 5. Re-scale grid and superimpose 6. Obtain area data w1th different thresholds wlthin each cell (area) 10 7. Qbtain shape conformation/information withln each cell (edge connectivity) 8. Determine sex characteristic if necessary 9. Determine fat distribution within each cell for each view 10. Determine conformation/muscular1ty within each cell 15 11. Compute grade (reference to database/algorithm on raw data) 12. Compute fat/conformation interaction 13. Compute overall yield 14. Compute yleld within each tell (primals) lS. Output data ~stamp carcass/sort carcass~

',.

. . :
. . .

.
: . ' .
.

Claims (8)

Claims

1. A method of grading carcasses after slaughter characterised in that it comprises the steps of checking for the presence of a carcass in the field of view of a camera, exposing an image of the carcass from a plurality of different viewpoints, determining a plurality of dimensions of said carcass from said images and comparing said dimensions with stored values to determine the overall grading of said carcass.

2. A method of grading carcasses after slaughter as claimed in claim 1, characterised in that it further comprises the step of checking that the orientation of said carcass with respect to the camera is in accordance with a predetermined arrangement.

3. A method of grading carcasses after slaughter as claimed in claim 1 or claim 2 wherein at least one of said images is exposed from a lateral viewpoint.

4. A method of grading carcasses after slaughter as claimed in any one of the preceding claims characterised in that at least one of said images is exposed from a dorsal viewpoint.

5. A method of grading carcasses after slaughter as claimed in any one of the preceding claims characterised in that at least one of said images is exposed from a posterior/anterior viewpoint.

6. A method of grading carcasses after slaughter as claimed in any one of the preceding claims characterised in that it includes the step of weighing the carcass.

7. A method of grading carcasses after slaughter as claimed in claim characterised in that it includes the step of determining the sex of the from which the carcass is derived.

8. Apparatus for the grading carcasses after slaughter characterised in that it comprises a plurality of video cameras (C1,C2) adapted to be positioned adjacent to a slaughter line (R) to expose an image of a carcass (P) on said slaughter line from a plurality of different viewpoints, signal processing means to derive from said images a plurality of parameters characteristic of said carcass, storage means to store a corresponding plurality of parameters derived from prior measurement of reference carcasses comparator means to compare said plurality of parameters with said corresponding plurality of parameters to derive a further parameter indicative of the grade of said carcass and indicator means to provide an indication of the magnitude of said further parameter.