Vision-based method for tracking meat cuts in slaughterhouses

Larsen, Allan; Hviid, M.; Jørgensen, Mikkel Engbo; Larsen, Rasmus; Dahl, Anders Bjorholm

doi:10.1016/j.meatsci.2013.07.023

Cited by 11 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CT has been employed to evaluate the tissue composition of pork carcasses using the differences in the density of individual tissues (adipose, muscular, and skeletal; Vester‐Christensen and others ). Recently, the image analysis technology has been used to track the elements of the dressing and fabrication line (Larsen and others ). The 3‐D image analysis has been successfully employed in the classification of large slaughter animals according to the EUROP grading system VBS 2000, BCC‐2, MAC i MAC‐2 systems; Borggaard and others ; Cannell and others ; Steiner and others ; Branschied and others ; Rius‐Vilarrasa and others ; Craigie and others ).…”

Section: Resultsmentioning

confidence: 99%

A Potential Use of 3‐D Scanning to Evaluate the Chemical Composition of Pork Meat

Adamczak

Chmiel

Florowski

et al. 2015

Journal of Food Science

View full text Add to dashboard Cite

The use of the 3-D scanning method in industrial practice will allow to evaluate the chemical composition of meat in online mode on a dressing and fabrication line and in a rapid, noninvasive manner. The control of the raw material using the 3-D scanning will allow to make visual assessment more objective and will enable optimal standardization of meat batches prior to processing stage. It will ensure not only the repeatability of product quality characteristics, but also optimal use of raw material-lean and fat meat. The knowledge of chemical composition of meat is essential due to legal requirements associated with mandatory nutrition facts labels on food products.

show abstract

Section: Resultsmentioning

confidence: 99%

A Potential Use of 3‐D Scanning to Evaluate the Chemical Composition of Pork Meat

Adamczak

Chmiel

Florowski

et al. 2015

Journal of Food Science

View full text Add to dashboard Cite

show abstract

“…However, these technologies were not applicable for compressed images such as images from the cellular phone, digital camera, and Internet as input. Object detection is also applied for meat cut traceability using radio frequency identification (RFID) and physical tagging which seem promising for block chain technology (Larsen et al, 2014). Other than computer vision algorithms, different machine learning techniques are used by researchers to classify meat cut, which includes extensive feature extraction process and often hard to generalize.…”

Section: Discussionmentioning

confidence: 99%

Using Deep Learning Neural Network in Artificial Intelligence Technology to Classify Beef Cuts

Borhan

Zhang

et al. 2021

Front. Sens.

View full text Add to dashboard Cite

The objective of this research was to evaluate the deep learning neural network in artificial intelligence (AI) technologies to rapidly classify seven different beef cuts (bone in rib eye steak, boneless rib eye steak, chuck steak, flank steak, New York strip, short rib, and tenderloin). Color images of beef samples were acquired from a laboratory-based computer vision system and collected from the Internet (Google Images) platforms. A total of 1,113 beef cut images were used as training, validation, and testing data subsets for this project. The model developed from the deep learning neural network algorithm was able to classify certain beef cuts (flank steak and tenderloin) up to 100% accuracy. Two pretrained convolution neutral network (CNN) models Visual Geometry Group (VGG16) and Inception ResNet V2 were used to train, validate, and test these models in classifying beef cut images. An image augmentation technique was incorporated in the convolution neutral network models for avoiding the overfitting problems, which demonstrated an improvement in the performance of the image classifier model. The VGG16 model outperformed the Inception ResNet V2 model. The VGG16 model coupled with data augmentation technique was able to achieve the highest accuracy of 98.6% on 116 test images, whereas Inception ResNet V2 accomplished a maximum accuracy of 95.7% on the same test images. Based on the performance metrics of both models, deep learning technology evidently showed a promising effort for beef cuts recognition in the meat science industry.

show abstract

“…Marking or tagging of the viscera are required to enable full carcass feedback to producers. The use of electronic tags, which would allow identification of individual pigs, and the rapid development in image analysis or machine learning, show promise in improving traceability and could allow automated data recording for pig health and welfare at high line speeds [57,58]. Automated detection of different lesions at MI is a promising new approach in research which is being currently being investigated for a variety of indicators such as lung, heart, liver, skin, ear and tail lesions as well as bursitis [38,[59][60][61].…”

Section: Future Directionsmentioning

confidence: 99%

Multi-Stakeholder Focus Groups on Potential for Meat Inspection Data to Inform Management of Pig Health and Welfare on Farm

2019

View full text Add to dashboard Cite

Meat inspection (MI) findings can act as a valuable source of information on pig health and welfare. The PIG WELFare INDicators (PIGWELFIND) project (Research Stimulus Fund 11/S/107) was developed to progress the development of ante and post mortem MI as a pig health and welfare diagnostic tool in Ireland. Three multi-stakeholder focus groups were organized to explore areas of conflict and agreement between stakeholders’ vision for including pig health and welfare indicators in MI and on how to achieve this vision. Each focus group consisted of eight stakeholders: pig producers, Teagasc pig advisors, pig processors, veterinarians involved in MI, private veterinary practitioners, and personnel with backgrounds in general animal health and welfare and food safety policy. In general, stakeholders expressed positive attitudes towards the use of MI data to inform pig health and welfare when standardization of recording and feedback is improved, and the MI system provides real-time benchmarking possibilities. Most emphasis was placed on health indicators as a first priority, while it was felt that welfare-related indicators could be included after practical barriers had been addressed (i.e., line speed/feasibility, standardization and training of meat inspectors, data ownership). Recommendations are made to further progress the development of MI as a pig health and welfare diagnostic tool and address some of these barriers.

show abstract

Vision-based method for tracking meat cuts in slaughterhouses

Cited by 11 publications

References 13 publications

A Potential Use of 3‐D Scanning to Evaluate the Chemical Composition of Pork Meat

A Potential Use of 3‐D Scanning to Evaluate the Chemical Composition of Pork Meat

Using Deep Learning Neural Network in Artificial Intelligence Technology to Classify Beef Cuts

Multi-Stakeholder Focus Groups on Potential for Meat Inspection Data to Inform Management of Pig Health and Welfare on Farm

Contact Info

Product

Resources

About