Edge Workpiece Detection Using Homogeneous Multi-Core Processing
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Solution Overview
Problem
Current machine vision industrial computers have slow operation speeds, leading to low detection speeds of workpieces.
Innovation Solution
Utilizing an edge computing device with a homogeneous multi-core architecture to perform image processing tasks, including preprocessing, rotation, and similarity calculations, which leverages multi-threading to efficiently utilize logical cores for rapid workpiece detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a machine vision industrial computer is used for workpiece detection, then the detection system can identify and clamp qualified workpieces, but the operation speed is slow resulting in low detection speed
Solution Approach 1:
The patent segments the workpiece detection process into multiple independent tasks that can be executed in parallel on different logical cores. The detection process is divided into image acquisition, preprocessing, feature extraction, and result analysis, with each stage capable of concurrent execution across multiple cores, thereby improving overall detection speed while maintaining operational efficiency
Solution Approach 2:
The patent transitions from single-core sequential processing to multi-core parallel processing by utilizing the dimensional advantage of multiple logical cores. This dimensional change in processing architecture allows simultaneous execution of multiple detection tasks, fundamentally improving operation speed without compromising detection accuracy
2Productivity
If traditional machine vision industrial computers are used, then workpiece detection can be performed, but the system fails to efficiently utilize multi-core processors resulting in low processing efficiency
Solution Approach 1:
The patent implements dynamic task allocation and scheduling mechanisms that adaptively assign detection tasks to available logical cores based on system load and task priority. This dynamic approach ensures optimal utilization of multi-core resources, maximizing processing efficiency while managing system complexity through flexible resource orchestration
Solution Approach 2:
The patent changes the operational parameters of the processing system by configuring thread affinity, priority levels, and resource allocation settings to optimize multi-core utilization. By adjusting these parameters, the system achieves high processing efficiency on multi-core architectures without requiring complex hardware modifications
Data Source
AI summary
A method for detecting workpiece based on homogeneous multi-core architecture is illustrate. The method comprises: obtaining detecting images of detecting workpieces; identifying detecting areas of the detecting workpieces in the detecting images; dividing the preset rotation angle to obtain the rotation accuracy and initial rotation angles; based on each of the initial rotation angles, rotating the detecting areas to obtain a rotation area of each of the initial rotation angles; calculating similarity values between each of the rotation areas and a preset qualified area, and determining a largest similarity value as the target similarity value; and when the rotation accuracy is greater than or equal to a preset accuracy, identifying whether the detecting workpiece is a qualified workpiece according to the target similarity value and a preset similarity threshold.