Log Image Matching for Tamper-Proof Wood Chain Tracking
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Solution Overview
Problem
Conventional wood processing systems face issues with low-quality image capture of logs due to severe environment conditions at the harvesting stage, leading to unreliable identification, categorization, and tracking, and are prone to manual errors and tampering.
Innovation Solution
An automated system using imaging devices at a sorting station to capture images of logs from oblique angles, coupled with a data processing arrangement for log identification and quality determination, and a machine learning model for reliable tracking and tamper-proof log identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If imaging devices are used at the harvesting stage to capture images of logs, then log identification and tracking can be performed, but the image quality is low due to severe environment conditions
Solution Approach 1:
The system captures images of log ends at the harvesting stage and stores them in a database before the logs undergo further processing. These pre-captured images serve as reference data for later identification and tracking at downstream stages, enabling reliable log identification without requiring high-quality images at the point of use.
Solution Approach 2:
The system creates digital copies of log end images and stores them in a database arrangement. These image copies are then used for identification and tracking purposes throughout the wood processing chain, eliminating the need to handle physical logs for identification and allowing multiple uses of the same image data.
2Adaptability or versatility
If manual intervention is used to determine log quality and volume, then flexibility in assessment can be achieved, but errors and human biases increase
Solution Approach 1:
The system replaces manual visual inspection with automated imaging devices and data processing arrangements. Cameras capture images of logs at various stages, and computer algorithms automatically analyze these images to determine log quality characteristics and volume, eliminating human error and bias while maintaining consistent assessment criteria.
Solution Approach 2:
The system enables logs to be automatically identified and tracked through their own unique visual features (such as natural markings on the log end). The imaging and data processing system performs self-identification without requiring human intervention, making the process both automated and consistent.
3Ease of manufacture
If physical tags are fastened on logs to indicate fell place, then simple identification can be achieved, but the tags are easy to manipulate and not tamper proof
Solution Approach 1:
The system replaces physical tags with digital image copies stored in a database. Instead of attaching physical identifiers to logs, the system captures and stores digital images of log ends, which serve as permanent, tamper-proof identification records that cannot be physically altered or removed from the log.
Solution Approach 2:
The system substitutes mechanical physical tagging with an optical and digital identification system. Cameras capture images of logs, and these images are stored and processed digitally throughout the wood processing chain, eliminating the need for physical tags and providing tamper-proof tracking through immutable digital records.
Data Source
AI summary
A system (100A) to track logs in a wood processing chain, includes a database arrangement (102) that includes pre-recorded image of a given log, wherein the given log is associated with log identification information. The system further includes a plurality of imaging devices implemented at a sorting station. The plurality of imaging devices (104) is configured to capture a first set of images from at least a first prespecified oblique angle. The system further includes a data processing arrangement (106) that is configured to: identify the given log at the sorting station; compare the at least one pre-recorded image with the captured first set of images at the sorting station in order to find an optimum image from the compared images for identification of the given log; determine a plurality of physical characteristics; and append the log identification information with the determined physical characteristics of the given log.