Optical Symbol Detection Using Descriptor Maps for Low-Latency Localization
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Solution Overview
Problem
Existing 2D symbol reading systems face challenges in rapidly identifying and accurately differentiating barcodes from varying backgrounds and lighting conditions, with increased processing demands due to higher image resolutions, leading to latency and computational inefficiencies.
Innovation Solution
A system utilizing a convolutional neural network with a low-density descriptor map, based on S-transform operations, to autonomously detect and localize machine-readable symbols, employing feature extraction and coarse estimation engines for efficient symbol detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image resolution is increased to improve symbol detection accuracy, then measurement precision is improved, but processing time increases and productivity deteriorates
Solution Approach 1:
The patent divides the image processing task into multiple stages: initial scanning at reduced resolution to identify potential symbol regions, followed by focused processing only on those specific regions at full resolution. This segmentation allows the system to maintain high detection accuracy while significantly reducing overall processing time and computational load.
Solution Approach 2:
The system performs partial processing by applying full-resolution analysis only to regions containing potential symbols rather than processing the entire high-resolution image. This partial action approach maintains measurement precision for symbol detection while reducing the total computational burden and improving processing throughput.
2Measurement precision
If image processing complexity is increased to improve symbol detection accuracy, then measurement precision is improved, but computational power requirements increase and device complexity worsens
Solution Approach 1:
The patent segments the computational workload by implementing a two-stage processing architecture: a first stage using simplified algorithms for rapid candidate identification, and a second stage applying complex analysis only to identified candidates. This segmentation reduces overall device complexity while maintaining high detection accuracy.
Solution Approach 2:
The system introduces an intermediary processing stage that generates candidate symbol regions from the full image, which then serves as input for the final detection algorithm. This intermediary step filters out non-symbol regions early, reducing the complexity of subsequent processing while preserving detection accuracy.
Data Source
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AI summary
An optical symbol detector has an input operative to receive a captured image frame. The detector includes a feature extractor engine coupled to the input to produce a downscaled descriptor map based on the captured image frame, the descriptor map including a convolutional map of blocks of the captured image frame. The detector further includes a coarse estimator engine coupled to an output of the feature extractor engine to produce a detection indication and localization information of an optical pattern indicative of a machine-readable symbol based on the downscaled descriptor map.