Non-intrusive Pop-up Window Detection via Image Differential Analysis
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Solution Overview
Problem
In automated industrial equipment, unexpected pop-up windows on screens can obscure production parameters, leading to operational interruptions and reduced efficiency, as existing non-intrusive devices struggle to recognize and manage these windows.
Innovation Solution
A non-intrusive detection method and device that retrieves screen images, compares them to presets, generates differential images, applies edge detection and contour analysis using the Canny and Douglas-Peucker algorithms, and performs optical character recognition to identify pop-up windows and buttons, thereby automatically detecting and interpreting their data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If non-intrusive devices are used to monitor equipment screens remotely, then manual operations are reduced and automation is improved, but pop-up windows can obscure production parameters causing detection failures and operational interruptions
Solution Approach 1:
The system performs preliminary actions by detecting pop-up windows before they obstruct critical production parameters. The image comparison algorithm continuously monitors the screen and identifies pop-up windows early in their appearance, allowing the system to alert operators or automatically handle them before they interfere with data collection and processing operations.
Solution Approach 2:
The system implements feedback mechanisms by comparing current screen images with historical images and analyzing differences to detect pop-up windows. The image comparison algorithm provides continuous feedback about screen changes, enabling the system to adapt to different equipment interfaces and maintain reliable detection even as equipment updates occur.
2Productivity
If traditional detection methods are used without pop-up window recognition, then device complexity is low, but productivity decreases due to operational interruptions
Solution Approach 1:
The system replaces manual mechanical detection methods with automated image processing algorithms. Instead of operators manually monitoring screens for pop-up windows, the system uses computer vision technology to automatically detect, identify, and manage pop-up windows, significantly improving operational efficiency while the modular architecture keeps complexity manageable.
Solution Approach 2:
The system creates digital copies of screen images for comparison and analysis. By capturing and storing historical screen images, the system can compare them with current images to detect changes indicating pop-up windows, eliminating the need for complex real-time analysis of raw screen data and improving detection efficiency.
3Measurement precision
If image comparison is performed to detect pop-up windows, then detection accuracy is improved, but processing time increases due to image analysis requirements
Solution Approach 1:
The system extracts only the essential information needed for pop-up window detection from full screen images. By focusing image comparison on specific regions where pop-up windows are likely to appear and using difference analysis to identify changes, the system achieves high detection accuracy while minimizing processing time by avoiding analysis of entire screen images.
Solution Approach 2:
The system performs partial image comparison by focusing on key areas of the screen where pop-up windows are most likely to appear. Rather than analyzing every pixel of the entire screen, the algorithm concentrates computational resources on regions of interest, achieving sufficient detection accuracy with reduced processing time and resource consumption.
Data Source
AI summary
A non-intrusive detection method for detecting at least one pop-up window button of the pop-up window includes the following steps: retrieving a screen image on a display device; comparing the screen image with a preset screen image and generating a differential image area according the screen image and the preset screen image; determining the differential image area as the pop-up window when the differential image area is greater than an image area threshold value; selecting a plurality of contour lengths of the pop-up window matching up with a contour length threshold value by Canny edge detector; and analyzing the contour lengths according to Douglas-Peucker algorithm and an amount of endpoints to generate a contour edge corresponding to the pop-up window button.