Backfinned Pork Loin Detection via Image Analysis
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Solution Overview
Problem
The meat processing industry faces inefficiencies in sorting meat products due to reliance on weight-based sorting, which can misroute backfinned loins and fail to accurately distinguish between backfinned and non-backfinned loins, leading to suboptimal processing.
Innovation Solution
An advanced feature detection system utilizing an image capture device, a transport system, and an iteratively trained machine learning model to identify and classify features such as backfinning in meat samples, enabling accurate sorting and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If weight-based sorting is used, then sorting can be performed with simple equipment, but backfinned loins are misrouted and cannot be properly processed
Solution Approach 1:
The patent replaces the mechanical weight-based sorting system with an optical/image-based detection system. Image capture devices photograph the loins, and machine learning models analyze these images to identify backfinning characteristics, substituting physical measurement with optical detection to achieve more reliable classification.
Solution Approach 2:
The patent introduces an intermediary classification system between weight-based sorting and final processing. An image analysis system with machine learning models acts as an intermediate step to identify backfinned loins before they reach the processing line, preventing misrouting while maintaining overall system simplicity.
2Ease of operation
If weight-based sorting is used, then the sorting process is simple to operate, but measurement precision is insufficient to distinguish backfinned loins
Solution Approach 1:
The patent replaces manual or simple mechanical sorting operations with an automated image analysis system. The machine learning models automatically detect backfinning characteristics in images, eliminating the need for operators to manually identify these subtle features while maintaining ease of operation through automated decision-making.
Solution Approach 2:
The patent creates visual copies (images) of the loins for analysis. Instead of directly handling and examining physical loins, the system captures digital images that can be analyzed by machine learning models, allowing precise measurement without complicating the operational process.
3Measurement precision
If automated feature detection is implemented, then backfinned loins are accurately identified, but device complexity increases
Solution Approach 1:
The patent makes the image capture and analysis system multi-functional. The same image capture devices and machine learning models can detect various features beyond just backfinning, such as loin quality, size, and other characteristics, justifying the increased device complexity through multiple uses.
Solution Approach 2:
The patent implements feedback loops where the machine learning models are continuously trained and improved using validation sample sets. The system learns from its errors and improves its detection accuracy over time, making the increased complexity worthwhile by achieving high measurement precision.
4Measurement precision
If iterative model training is performed, then detection accuracy is improved, but loss of time increases during model development
Solution Approach 1:
The patent performs preliminary actions by pre-training the machine learning models using large validation sample sets before deployment. This preliminary training reduces the need for extensive retraining later, as the models are already optimized for detecting backfinning characteristics in production environments.
Solution Approach 2:
The patent uses feedback from validation samples to continuously improve the models. By systematically training on diverse samples and evaluating performance, the system achieves high accuracy while managing training time through efficient iterative improvement rather than exhaustive training.
Data Source
AI summary
The disclosure relates to a system and method for identifying one or more features in a meat sample. In one embodiment, the system and method disclosed herein uses automated methods to identify a backfinned pork loin and a non-backfinned pork loin. The system and method disclosed herein comprises imaging a meat sample with an imaging device to obtain one or more images; and using an iteratively trained feature detection model to detect and identify one or more features, including the presence or absence of the features in the images of the meat sample.