Machine Learning Code Reading for Curved, Reflective Surfaces
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Solution Overview
Problem
Optically readable codes embedded in the surfaces of plant and animal products, such as fruits and vegetables, suffer from low contrast, distortions, and reflections, making them difficult to read using consumer devices like smartphones.
Innovation Solution
A machine learning model is trained using transformed reference images to enhance contrast and reduce distortions and reflections, enabling accurate decoding of optically readable codes on diverse surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If optically readable codes are embedded into the surface of plant or animal products using a laser, then the codes can be applied directly to the product surface, but the contrast between the code and surrounding tissue is lower making them more difficult to read
Solution Approach 1:
The patent applies preliminary image processing transformations to the captured code image before decoding. The system performs contrast enhancement, distortion correction, and reflection reduction as preprocessing steps to improve the readability of laser-etched codes on product surfaces.
2Ease of manufacture
If optically readable codes are applied to curved surfaces such as fruits and vegetables, then the codes can be applied to the product surface, but the codes become distorted making them difficult to read
Solution Approach 1:
The system performs distortion correction as a preliminary processing step before decoding. Image processing algorithms detect and correct the geometric distortions caused by applying codes to curved surfaces, restoring the code to its original rectangular form for accurate reading.
3Ease of manufacture
If optically readable codes are applied to smooth surfaces, then the codes can be applied to the product surface, but reflections from ambient light occur during reading making reading more difficult
Solution Approach 1:
The system performs reflection reduction as a preliminary image processing step. Algorithms detect and remove reflection artifacts from the captured code image, enhancing the visibility of the code elements by suppressing spurious light reflections from smooth surfaces.
4Stability of the object's composition
If the surfaces of fruits and vegetables are uneven with specks and spots or bumps, then the natural surface characteristics are preserved, but the inconsistencies and bumps make codes difficult to read
Solution Approach 1:
The system performs noise reduction and surface artifact removal as preliminary processing steps. Image processing algorithms distinguish between code elements and surface irregularities such as specks, spots, and bumps, removing the latter while preserving the code structure for accurate decoding.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The model generates transformed images with improved code readability, reducing decoding errors and ensuring reliable code recognition on various objects.
Implementation Method 1
sensing means (camera 100) for optically detecting a code image reflected from the optically readable code
Data Source
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AI summary
The present invention relates to the technical field of the marking of articles by means of optically readable codes and of decoding the codes. Such a code is introduced into a surface of an article. The code is decoded on the basis of a transformed captured image of the code. The transformed captured image is generated from at least one captured image of the code using a machine learning model. The model is trained to generate a transformed captured image from at least one captured image and the readout of the optically readable code leads to less decoding errors than the readout of the code in the at least one captured image. The present invention relates to a method for training the machine learning model and to a method, a system and a computer program product for decoding a code using the trained machine learning model.