Wood Knot Detection via Circularity and Threshold Analysis
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Solution Overview
Problem
Conventional methods for sorting wood veneers into outer and inner layers based on knots and defects are inaccurate due to human error and rely solely on color shading detection, leading to inconsistencies and low productivity.
Innovation Solution
A method involving photography and image processing to calculate degrees of circularity, vary color shading threshold levels, and integrate images to accurately detect and determine knot configurations and candidates, including dead knots, using a combination of image processing techniques and multi-camera systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If naked eye determination is used for sorting veneers, then the sorting process can be performed with simple equipment, but inaccuracy and inconsistencies occur due to human error
Solution Approach 1:
The patent replaces the mechanical human eye determination system with an optical image processing system. A camera captures images of veneers, and a computer processes these images to automatically detect knots and defects, eliminating human error and inconsistency while maintaining relatively simple equipment requirements.
Solution Approach 2:
The patent creates a digital copy (image) of the veneer surface and analyzes this copy rather than directly examining the physical veneer. This allows repeated measurements, storage of data, and processing by algorithms that can consistently identify knots and defects without human variation.
2Measurement precision
If conventional image processing based on color shading is used, then the detection process is simple, but configuration of knots and defects cannot be accurately detected
Solution Approach 1:
The patent segments the image processing into multiple stages: threshold processing to identify potential knots, circularity calculation to filter false positives, and configuration analysis to determine knot types. This systematic segmentation allows accurate detection while keeping each individual processing step relatively simple.
Solution Approach 2:
The patent changes multiple parameters for comprehensive analysis: color shading thresholds, circularity degrees, and configuration patterns. By varying these parameters and analyzing their combinations, the system accurately identifies knot configurations without relying on a single simple detection method.
3Productivity
If manual sorting methods are used, then equipment requirements are minimal, but productivity is low due to inability to increase conveyor speed
Solution Approach 1:
The patent replaces manual visual inspection with an automated optical detection system that operates at high speeds. The camera captures images rapidly, and computer processing occurs in real-time, enabling conveyor speeds to be increased significantly while maintaining accurate sorting.
Solution Approach 2:
The patent enables continuous operation of the sorting process without interruption. The image processing system operates continuously as veneers pass through, eliminating the stop-start nature of manual inspection and allowing maintainably high conveyor speeds throughout the entire sorting process.
Data Source
AI summary
The photographing means of the present apparatus photographs a piece of wood, while the image processing means calculates degrees of circularity from the photographed images of the piece of wood, and detects images with significant degrees of circularity as knots. In addition, the image processing means 1 clips a portion including a knot from the photographed image of the piece of wood, deems a portion clipped at a predetermined threshold among the color spaces of each pixel of the clipped portions to be a blackened portion, and determines black portions with a high proportion of a number of pixels of the blackened portion to the number of pixels of the clipped portion of the knot as dead knots.