SHRIMP PROCESSING SYSTEM AND METHODS

• US 20140168411A1
• Filed: 11/21/2013
• Published: 06/19/2014
• Est. Priority Date: 12/19/2012
• Status: Active Grant

• Current Assignee: Laitram LLC
• Original Assignee: Laitram LLC
• Inventors: Lapeyre, Robert S., Ledet, Charles J.
• Granted Patent: US 9,930,896 B2
• Time in Patent Office: Days
• US Class Current: 348/89
• CPC Class Codes: A22C 25/04 Sorting fish; Separating ice from fish packed in ice investigating fish G01N33/12
• CPC Class Codes: A22C 29/005 Grading or classifying shellfish or bivalves
• CPC Class Codes: A22C 29/02 Processing shrimps, lobsters or the like ; Methods or machines for the shelling of shellfish
• CPC Class Codes: A22C 29/023 Conveying, feeding or aligning shellfish
• CPC Class Codes: A22C 29/024 Opening, shelling or peeling shellfish
• CPC Class Codes: B07C 5/3422 using video scanning devices, e.g. TV-cameras
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06V 20/10 Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59

Abstract

Methods and systems using a vision system to process shrimp. The vision system captures images of samples of shrimps. The processor produces a digital image of the shrimps in the samples. Shrimps exiting a peeler are imaged to determine the number of tail segments in each. The shrimps are classified by the number of intact segments, and quality, yield, and throughput computed from the classification results. The processor can control operational settings of the peeler based on the classification results. In a larger system including other shrimp-processing equipment besides the peeler, other points along the processing path can be imaged by camera or sensed by other sensors to determine processing quality and to make automatic operational adjustments to the equipment.

Claims

1. A method for processing peeled shrimps, comprising:

depositing deheaded, peeled shrimps onto a support surface;

creating a digital image of the shrimps on the support surface;

estimating the percentage of full weight of each of the shrimps from the digital image;

classifying each of the shrimps into one of a plurality of classes according to the percentage of full weight of each of the shrimps.

2. The method of claim 1 wherein the shrimps are deposited side down on the support surface.

3. The method of claim 1 further comprising:

counting the number of shrimps classified in each of the plurality of classes.

4. The method of claim 3 comprising:

transporting shrimps on the support surface continuously in a conveying direction;

creating a series of digital images of different groups of the shrimps on the support surface;

accumulating the counts of the numbers of shrimps in each of the classes from the series of digital images.

5. The method of claim 4 further comprising:

calculating a moving average of the count for each class with a smoothing filter.

6. The method of claim 1 further comprising:

counting the number of shrimps in the digital image.

7. The method of claim 1 comprising:

estimating the percentage of full weight of each of the shrimps by determining the number of tail segments present in each of the shrimps from the digital image;

classifying each of the shrimps into one of four classes, wherein a first class includes shrimps having six full segments, a second class includes shrimps not in the first class having five and a half or more segments, a third class includes shrimps not in the first or second class having five full segments, and a fourth class includes shrimps not in the first, second, or third class.

8. The method of claim 3 further comprising:

calculating yield quality for each of the classes as the ratio of the count of shrimps in each class to the sum of the counts in all the classes.

9. The method of claim 8 further comprising:

reporting the yield quality of each of the classes.

10. The method of claim 3 further comprising:

determining the count of shrimps with attached shell from the digital image.

11. The method of claim 10 further comprising:

calculating peeling quality as one minus the count of shrimps with shell attached to the sum of the counts in all the classes.

12. The method of claim 11 further comprising:

reporting the peeling quality.

13. The method of claim 1 comprising:

estimating the percentage of full weight of each of the shrimps by determining the number of tail segments present in each of the shrimps by comparing a digital image of each of the shrimps to a digital model associated with each of the classes and determining the best match of the digital image to the digital model to classify each of the shrimps.

14. The method of claim 1 comprising:

estimating the percentage of full weight of each of the shrimps by determining the number of tail segments present in each of the shrimps from the arc of the shrimp from a head end to an opposite tail end.

15. The method of claim 14 wherein the number of tail segments is determined from the length of the arc.

16. The method of claim 14 wherein the number of tail segments is determined from the angular extent of the arc.

17. The method of claim 1 comprising:

estimating the percentage of full weight of each of the shrimps by determining the number of tail segments present in each of the shrimps from the ratio of the width of the shrimp at a wider head end to the width of the shrimp at a narrower opposite tail end.

18. The method of claim 1 comprising:

estimating the percentage of full weight of each of the shrimps by determining the number of tail segments present in each of the shrimps from pigmentation lines extending from the upper edge to the lower edge of the shrimp at the interface between contiguous segments.

19. The method of claim 1 comprising:

estimating the percentage of full weight of each of the shrimps by determining the number of tail segments present in each of the shrimps from indentations detected in the upper and lower edges of the shrimp.

20. The method of claim 1 comprising:

creating the digital image of the shrimps on the support surface disposed downstream of a peeling machine that peeled and deheaded the shrimps;

counting the number of shrimps classified into each of the plurality of classes;

calculating yield quality for each of the classes as the ratio of the count of shrimps in each class to the sum of the counts in all the classes;

adjusting one or more operational settings of the peeling machine as a function of the calculated yield quality for each of the classes.

21. The method of claim 1 comprising:

creating the digital image of the shrimps on the support surface disposed downstream of a peeling machine that peeled and deheaded the shrimps;

counting the total number of shrimps in the digital image;

counting the number of shrimps in the digital image with shell attached;

calculating peeling quality as one minus the count of shrimps with shell attached to the total number of shrimps in the digital image;

adjusting one or more operational settings of the peeling machine as a function of the calculated peeling quality.

30. The method of claim 1 further comprising:

estimating the weight and the six-segment weight of each of the shrimps from the digital image;

summing the estimated weights of each of the shrimps to compute an accumulated weight;

summing the estimated six-segment weights of each of the shrimps to compute an accumulated six-segment weight;

computing the yield by dividing the accumulated weight by the accumulated six-segment weight.

31. The method of claim 1 wherein the support surface comprises a conveyor for conveying the shrimp along a process path.

22. A shrimp-processing system comprising:

a shrimp peeling machine removing the heads and shells from shrimp to produce peeled shrimps;

a conveyor conveying the peeled shrimps from the shrimp peeling machine to downstream processing;

a vision system capturing a digital image of peeled shrimps;

a processor determining the number of tail segments present in each of the shrimps from the digital image and classifying each of the shrimps into one of a plurality of classes according to the number of tail segments present in each of the shrimps.

23. A shrimp-processing system as in claim 22 wherein the processor further computes yield values for each of the classes.

24. A shrimp-processing system as in claim 23 wherein the processor further sends control signals derived from the yield values to the shrimp peeling machine to adjust one or more operational settings of the shrimp peeling machine.

32. A shrimp-processing system as in claim 22 wherein the vision system captures a digital image of peeled shrimps on the conveyor.

25. A shrimp-processing system comprising:

a peeler for removing the shells and heads from unpeeled shrimps to produce peeled shrimps;

a feed system feeding unpeeled shrimps to the peeler;

a conveyor system conveying peeled shrimps from the peeler downstream along a process path;

one or more shrimp-processing machines downstream of the peeler in the process path acting on the peeled shrimps;

one or more sensors detecting one or more operational variables of the shrimp-processing machines or of the peeler or physical characteristics of the shrimp and producing sensor signals indicative of the one or more operational variables;

a processor receiving the sensor signals and deriving control signals from the sensor signals and sending the control signals to the peeler or the one or more shrimp-processing machines to adjust one or more operational settings.

26. A shrimp-processing system as in claim 25 wherein the one or more sensors include one or more vision systems capturing digital images of the shrimp along the process path.

27. A shrimp-processing system as in claim 26 wherein the one or more vision systems capture digital images of the shrimp on the conveyor system.

28. A shrimp-processing system comprising:

a shrimp peeling machine removing the heads and shells from shrimp to produce peeled shrimps;

a conveyor conveying the peeled shrimps from the shrimp peeling machine to downstream processing;

a vision system capturing a digital image of shrimps peeled by the shrimp peeling machine;

a processor determining the percentage of full weight of each of the imaged peeled shrimps from the digital image and classifying each of the imaged peeled shrimps into one of a plurality of classes according to the percentage of full weight of each of the imaged peeled shrimps.

29. A shrimp-processing system as in claim 28 wherein the vision system captures a digital image of shrimps on the conveyor.