Automated Pin Bone Removal via 3D Scanning and Curve Fitting
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Solution Overview
Problem
Current methods for removing undesirable tissues like bones and fat from food products, especially fish, are inefficient and costly, relying on manual processes or expensive x-ray technology, and struggle with accurate automated detection and removal without human intervention.
Innovation Solution
A system and method using three-dimensional modeling and scanning to identify and remove unwanted tissues by generating cross-sectional images, determining maximum thickness, and fitting a curve to estimate tissue points, allowing for automated cutting of these tissues without manual verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification and removal of pin bones is used, then removal accuracy can be achieved, but production throughput is significantly slowed down
Solution Approach 1:
The patent replaces the manual mechanical inspection and cutting process with an automated optical scanning system using visible light cameras and computer vision algorithms. The system captures images of the fish fillet, processes them through software to identify pin bone locations, and guides an automated cutter to remove the bones, thereby maintaining accuracy while dramatically increasing throughput.
2Measurement precision
If x-ray scanning technology is implemented to automatically locate bones, then detection accuracy is improved, but system cost and complexity increase significantly
Solution Approach 1:
The patent employs inexpensive visible light scanning equipment instead of costly x-ray machines. The system uses standard digital cameras or image sensors that capture reflected light from the fish fillet surface, processing the images through algorithms to detect pin bones. This approach achieves sufficient detection accuracy at a fraction of the cost of x-ray technology.
Solution Approach 2:
The patent changes the detection parameter from x-ray absorption characteristics to visible light reflection characteristics. By analyzing variations in light reflection patterns on the fish fillet surface, the system can identify pin bone locations without requiring expensive x-ray equipment, thus reducing system complexity and cost while maintaining detection capability.
3Device complexity
If computer vision alone is used to detect pin bones, then system cost is reduced, but detection reliability and accuracy decrease
Solution Approach 1:
The patent introduces image processing algorithms as an intermediary between the visible light scanning and pin bone detection. The software processes the captured images by analyzing pixel patterns, identifying characteristic features that indicate pin bone presence, and filtering out false positives. This computational intermediary enhances the reliability of detection while keeping the hardware simple and inexpensive.
Data Source
AI summary
A method for identifying and removing tissue from a food product that includes generating a three-dimensional model of a food product using a scanner and mapping the three-dimensional model onto the food product. The method also includes scanning the food product such that cross-sectional scanning images are generated based on the model, and, for each cross-sectional scanning image, determining a maximum thickness of the model and identifying a corresponding estimated tissue point, by using an identification method based on suitable characteristics of the food product model. The method includes fitting a curve to the estimated tissue points and generating a cut path based on the fitted curve, wherein the cut path defines an area of unwanted tissue that includes the estimated tissue points. The method further includes cutting the food product along the cut path, thereby, removing the area of unwanted tissue.