Automated Meat Identification via Computer Vision

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Solution Overview

Problem

Manual identification and sorting of meat cuts in meat processing facilities are labor-intensive, time-consuming, error-prone, and impact worker safety and productivity, leading to increased costs and reduced accuracy.

Innovation Solution

A meat identification system utilizing a video camera and classification system that captures image data of moving meat cuts, employing machine learning and depth sensors to accurately identify and sort cuts, reducing human error and fatigue, and integrating with conveyor systems for automated sorting.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual identification and sorting of meat cuts is used, then worker flexibility and adaptability are maintained, but labor intensity increases and productivity decreases

Engineering Contradiction:

Improveworker flexibilityVSAvoidsorting speed

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The patent replaces the manual mechanical sorting system with an automated computer vision system. The imaging system captures images of meat cuts, and the classification system uses machine learning algorithms to automatically identify and sort meat products, eliminating the need for workers to manually handle and sort meat cuts while significantly increasing sorting speed and productivity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Device complexity

If manual sorting is used, then system complexity remains low, but identification accuracy decreases due to worker fatigue and errors

Engineering Contradiction:

Improvesystem simplicityVSAvoididentification accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent replaces manual visual identification with an automated imaging and classification system. The imaging system captures high-resolution images of meat cuts, and the classification system uses machine learning models trained on extensive datasets to accurately identify different meat types, cuts, and qualities, achieving high identification accuracy without worker fatigue or errors

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent creates a digital copy of the meat cut appearance through imaging, replacing the need for workers to directly observe and identify physical meat cuts. The classification system processes these digital images to identify meat products, maintaining accuracy while eliminating human error and fatigue

Inventive Principle:

Principle #26Copying

3Productivity

If assembly line speed is increased to improve yield, then productivity improves, but identification accuracy and worker safety deteriorate

Engineering Contradiction:

Improveassembly line yieldVSAvoididentification accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent replaces manual identification with automated computer vision technology that can operate at high speeds without compromising accuracy. The imaging system captures images rapidly as meat cuts move along the conveyor, and the classification system processes these images in real-time using machine learning algorithms, enabling high assembly line speeds to maintain both productivity and identification accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent enables continuous automated identification throughout the entire assembly line process without interruption. The imaging system operates continuously as meat cuts move along the conveyor, and the classification system processes images in real-time, allowing the assembly line to maintain optimal speed while ensuring accurate identification of all meat products

Inventive Principle:

Principle #20Continuity of useful action

4Ease of manufacture

If manual sorting is used, then implementation cost remains low, but labor costs and time consumption increase

Engineering Contradiction:

Improveimplementation costVSAvoidsorting time

Core Design Contradiction:

Ease of manufactureVSLoss of time

Solution Approach 1:

The patent replaces time-consuming manual sorting with automated computer vision technology. The imaging system captures images of meat cuts as they move along the conveyor, and the classification system uses machine learning algorithms to automatically identify and sort products, dramatically reducing sorting time while the system can be integrated into existing facilities to control implementation costs

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20230343069A1Meat Identification System and Method

Publication Date: 2023.10.26 MARBLE TECH INC

AI summary

A meat identification system and method are disclosed. The system obtains at least one frame of image data of a cut of meat moving along a path, and identifies the cut of meat based upon the image data in the at least one frame. For this purpose, in one embodiment, the system includes a conveyor and an image acquisition system configured to obtain at least one frame (e.g., 5-15 frames, such as 10 frames) of image data of a cut of meat moving along the conveyor. The system predicts a class of meat based upon the image data to identify the cut of meat. In examples, the system also obtains weight data and depth data relating to the cuts of meat as the cuts move along the path, and can use the weight data and/or the depth data, in conjunction with the image data, to identify the cuts of meat.