Trace Detection Model Training with Line Segment Edge Features
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Solution Overview
Problem
Manual inspection of traces in electronic products is inefficient and prone to errors, affecting product yield and accuracy in determining trace standardization.
Innovation Solution
A method and device for training a trace detection model through line segment detection and edge feature extraction, using support vector machines or convolutional neural networks, to automate the inspection process and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection is used to determine trace standardization, then operational flexibility is maintained, but inspection efficiency is low and error rates are high
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated image processing system. The system captures images of traces using a camera, processes them through algorithms including line segment detection and feature extraction, and automatically determines compliance. This substitution eliminates human error and fatigue while maintaining consistent inspection criteria, thereby improving both efficiency and reliability simultaneously.
2Productivity
If automated image processing is implemented for trace detection, then inspection efficiency improves, but system complexity increases
Solution Approach 1:
The patent divides the complex trace detection task into multiple manageable modules: image acquisition module, preprocessing module (including registration and down-sampling), line segment detection module, feature extraction module, and compliance judgment module. Each module handles a specific aspect of the detection process, making the overall system more manageable and easier to implement despite the increased automation capability.
Solution Approach 2:
The patent performs preliminary processing steps before main detection, including image registration to align with standard images, down-sampling to reduce computational load, and pre-defined ROI identification. These preliminary actions prepare the data in advance, simplifying subsequent detection operations and reducing the complexity of the main processing algorithms.
3Measurement precision
If detailed feature extraction is performed on all image regions, then detection precision improves, but processing time increases
Solution Approach 1:
The patent applies different processing quality levels to different regions of the image. It identifies Regions of Interest (ROI) where traces are located and performs detailed line segment detection and feature extraction only in these regions. Other areas receive minimal or no processing. This localized approach maintains high detection precision for critical areas while significantly reducing overall processing time.
Solution Approach 2:
The patent extracts and processes only the essential features needed for trace detection, such as line segment endpoints, slopes, and lengths. It removes unnecessary image data and focuses computational resources on extracting the minimal sufficient feature set required for accurate compliance determination, thereby reducing processing time without sacrificing detection precision.
Data Source
AI summary
A non-transitory computer-readable storage medium, a trace detection device, and a device and method for training a trace detection model are described, relating to the technical field of machine learning. The training method comprises obtaining a sample image and a sample tag of the sample image; performing line segment detection on the sample image, and obtaining a line segment edge feature of the sample image; generating a training feature according to the line segment edge feature; and training a classification model according to the sample tag and the training feature to obtain a trace detection model (S140).