Shrimp processing system and methods

Patent metadata

Field	Value
Publication number	US-9930896-B2
Application number	US-201314086659-A
Country	US
Kind code	B2
Filing date	Nov 21, 2013
Priority date	Dec 19, 2012
Publication date	Apr 3, 2018
Grant date	Apr 3, 2018

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.

First claim

What is claimed is:

1. A processor-implemented method for processing peeled shrimps, comprising:
depositing deheaded, peeled shrimps received from a peeling machine onto a support surface;
creating a digital image of the shrimps on the support surface with a vision system;
sending the digital image to a processor;
estimating, by the processor, the percentage of full weight of each of the shrimps from the digital image;
classifying, by the processor, each of the shrimps into one of a plurality of classes according to the percentage of full weight of each of the shrimps;
compiling, by the processor, peeling statistics from the estimates of percentage of full weight of the shrimps; and
adjusting one or more operational settings of the peeling machine based on the peeling statistics.

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, by the processor, 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 with a vision system;
accumulating, by the processor, 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, by the processor, a moving average of the count for each class with a smoothing filter.

6. The method of claim 1 further comprising:
counting, by the processor, 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, by the processor, 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, by the processor, the count of shrimps with attached shell from the digital image.

11. The method of claim 10 further comprising:
calculating, by the processor, 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, by the processor, the number of shrimps classified into each of the plurality of classes;
calculating, by the processor, 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, by the processor, the total number of shrimps in the digital image;
counting, by the processor, the number of shrimps in the digital image with shell attached;
calculating, by the processor, 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.

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.

25. 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

Assignees

• Laitram Llc

Inventors

• Ledet Charles J
• Lapeyre Robert S

Classifications

• G06F18/24

  Classification techniques · CPC title

• A22C29/005

  Grading or classifying shellfish or bivalves · CPC title

• B07C5/3422

  using video scanning devices, e.g. TV-cameras · CPC title

• G06K9/6267

  Physics · mapped topic

• A22C29/02

  Processing shrimps, lobsters or the like {; Methods or machines for the shelling of shellfish} · CPC title