AI Symbol Reader with Segmented LED Zones
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Solution Overview
Problem
Machine-readable symbol readers face difficulties in reading Direct Part Marking (DPM) symbols due to low contrast and varying environmental conditions, requiring time-consuming configuration of optimal reading conditions for each installation.
Innovation Solution
A machine-readable symbol reader system utilizing a combination of a variable focus lens, deep learning networks, and greedy search algorithms to autonomously determine and configure optimal reading parameters, including illumination patterns and focus settings, during an initial learning phase for improved decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If flood illumination is used to illuminate the entire machine-readable symbol, then the entire symbol is simultaneously illuminated, but the reading time increases and the system becomes more complex
Solution Approach 1:
The patent segments the illumination into multiple independent LED zones that can be selectively activated. Instead of illuminating the entire symbol area simultaneously, the system divides the illumination into discrete zones and activates only those needed for the current reading task, reducing overall illumination time while maintaining reading reliability.
Solution Approach 2:
The patent implements periodic illumination where LED zones are activated in sequences rather than continuously. The illumination system uses periodic activation patterns where different LED zones are illuminated at different time intervals during the reading process, reducing total illumination time while ensuring sufficient light is available when needed.
2Adaptability or versatility
If multiple LED zones are used to provide flexible illumination patterns, then the adaptability to different symbols improves, but the device complexity increases
Solution Approach 1:
The illumination system is divided into multiple independently controllable LED zones arranged in a grid pattern. Each zone can be selectively activated or deactivated based on the symbol being read, allowing flexible illumination patterns. This segmentation provides adaptability to different symbol types and positions while managing complexity through modular design.
Solution Approach 2:
The patent implements dynamic control of LED zones where the illumination pattern can change in real-time based on the detected symbol characteristics. The system dynamically adjusts which LED zones are active, allowing adaptation to different symbol locations, orientations, and types. This dynamic behavior enhances versatility while the programmed control logic manages the complexity.
3Measurement precision
If the machine-readable symbol reader is configured for specific reading conditions at installation, then the decoding accuracy improves, but the setup time and ease of operation decrease
Solution Approach 1:
The patent implements self-service configuration where the system automatically detects symbol characteristics and configures optimal reading parameters without manual intervention. The LED zones are selectively activated based on automated detection of symbol position, orientation, and type. This self-service approach maintains high decoding accuracy while eliminating complex manual setup procedures.
Solution Approach 2:
The system performs preliminary detection and analysis of the machine-readable symbol before initiating the reading process. Characteristic detection of the symbol occurs in advance, allowing the system to pre-configure the appropriate LED illumination patterns and reading parameters. This preliminary action ensures optimal decoding accuracy is achieved automatically, improving ease of operation.
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 system significantly enhances the ability to read DPM symbols by automatically determining optimal reading conditions, reducing the time and effort required for setup and improving decoding success rates across varying environments.
Implementation Method 1
Machine-readable symbols are typically composed of patterns of high and low reflectance areas
Implementation Method 2
an imager subsystem comprising an image sensor and a variable focus lens
Data Source
AI summary
Systems and methods for establishing optimal reading conditions for a machine-readable symbol reader. A machine-readable symbol reader may selectively control reading conditions including lighting conditions (e.g., illumination pattern), focus, decoder library parameters (e.g., exposure time, gain), etc. Deep learning and optimization algorithms (e.g., greedy search algorithms) are used to autonomously learn an optimal set of reading parameters to be used for the reader in a particular application. A deep learning network (e.g., a convolutional neural network) may be used to locate machine-readable symbols in images captured by the reader, and greedy search algorithms may be used to determine a reading distance parameter and one or more illumination parameters during an autonomous learning phase of the reader. The machine-readable symbol reader may be configured with the autonomously learned reading parameters, which enables the machine-readable symbol reader to accurately and quickly decode machine-readable symbols (e.g., direct part marking (DPM) symbols).