Multi-Core Vision System for Wide Conveyor Symbology Decoding
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Solution Overview
Problem
Conventional vision systems face challenges in capturing and decoding symbology, such as barcodes, over wide conveyor lines due to limited field of view and high object throughput, requiring high frame rates and processing speeds that are often unattainable with current technology.
Innovation Solution
A vision system equipped with a multi-core processor, high-speed imager, field of view expander, and auto-focus lens, along with an imager-connected pre-processor, which enables efficient image acquisition and processing by dividing the field of view into partial images and optimizing heat dissipation, allowing for effective decoding of symbology across a wide range of applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a single ID reader with conventional imager is used, then the device complexity is low, but the field of view is insufficient to cover wide conveyor lines
Solution Approach 1:
The patent divides the wide field of view into multiple narrower strips using a field of view expander (FOVE) that splits the image into a plurality of partial images. This segmentation allows a single imager to cover a wide conveyor line width while maintaining sufficient resolution for each strip, avoiding the need for multiple cameras across the width.
Solution Approach 2:
The FOVE optically expands the native FOV by transforming the one-dimensional image acquisition into a multi-dimensional approach, creating multiple vertical strips from a single imager's field of view. This dimensional transformation enables wide coverage without proportionally increasing device complexity.
2Area of stationary object
If multiple cameras are used across the width of the line, then the field of view coverage is sufficient, but the device complexity and cost increase significantly
Solution Approach 1:
Instead of using multiple separate cameras, the patent segments the field of view of a single camera into multiple strips using optical elements (FOVE). This achieves the same coverage as multiple cameras while using only one imager, significantly reducing device complexity and cost.
Solution Approach 2:
A single imager is designed to perform multiple functions by capturing multiple strips of the conveyor line simultaneously through the FOVE, replacing what would traditionally require multiple dedicated cameras. This multi-functionality reduces the overall system complexity.
3Area of stationary object
If a field of view expander is used to expand the FOV width, then the field of view coverage increases, but the field of view height decreases requiring higher frame rates
Solution Approach 1:
The system dynamically adjusts the frame rate based on object throughput speed. The processor monitors conveyor line speed and adjusts the imager's frame rate accordingly, increasing it when objects move faster to ensure proper capture timing. This dynamic adaptation allows the system to handle high throughput without requiring continuously high frame rates.
Solution Approach 2:
The system uses feedback from the conveyor line speed sensor to adjust image acquisition parameters. The processor receives feedback about object movement speed and modifies the frame rate and processing operations accordingly, ensuring optimal performance at varying throughput conditions.
4Productivity
If high frame rates are used to capture fast-moving objects, then the object throughput handling improves, but the processing speed requirements increase beyond current technology limits
Solution Approach 1:
The patent segments the image processing into multiple independent operations that can be executed in parallel across multiple processor cores. Each core handles specific tasks such as different strip images or different processing operations simultaneously, effectively multiplying the processing throughput to match high frame rate acquisition.
Solution Approach 2:
The system performs preliminary image processing operations at the imager level before data reaches the main processor. The pre-processor handles initial tasks such as noise filtering and basic image quality assessment, reducing the processing burden on the main processor and enabling higher frame rates to be handled within acceptable processing power constraints.
5Measurement precision
If the imager captures high-resolution images, then the symbology decoding accuracy improves, but the processing time increases reducing throughput
Solution Approach 1:
The patent segments the image data into multiple strips that are processed in parallel by different processor cores. Each core processes a specific strip independently, maintaining high resolution for accurate symbology decoding while reducing the sequential processing time. This parallel segmentation allows both high accuracy and high throughput to coexist.
Solution Approach 2:
The system applies different processing quality levels to different regions of the image based on local requirements. Areas with symbology receive high-resolution processing while other areas may be processed at lower resolution or skipped entirely, optimizing the balance between decoding accuracy and processing throughput.
Data Source
AI summary
This invention provides a vision system camera, and associated methods of operation, having a multi-core processor, high-speed, high-resolution imager, FOVE, auto-focus lens and imager-connected pre-processor to pre-process image data provides the acquisition and processing speed, as well as the image resolution that are highly desirable in a wide range of applications. This arrangement effectively scans objects that require a wide field of view, vary in size and move relatively quickly with respect to the system field of view. This vision system provides a physical package with a wide variety of physical interconnections to support various options and control functions. The package effectively dissipates internally generated heat by arranging components to optimize heat transfer to the ambient environment and includes dissipating structure (e.g. fins) to facilitate such transfer. The system also enables a wide range of multi-core processes to optimize and load-balance both image processing and system operation (i.e. auto-regulation tasks).