Automated Crack Detection in Mobile Screens via Edge Proximity
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Solution Overview
Problem
Current methods for detecting cracks in mobile device screens are either slow and cumbersome for manual inspection or prone to high false-positive rates with automated systems, necessitating an improved automated solution for accurate crack detection.
Innovation Solution
An automated system that uses image analysis techniques, including edge detection, proximity analysis, and Hough transform to identify genuine cracks by distinguishing them from artifacts, and calculates a crack assessment score based on identified line segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to detect cracks, then accuracy can be maintained, but productivity is reduced and time is lost
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated image processing system that uses edge detection algorithms, proximity analysis, and Hough transform to automatically identify cracks in device screens, thereby maintaining detection accuracy while significantly improving inspection speed and productivity
Solution Approach 2:
The patent introduces an intermediary image processing system that acts as a mediator between the device screen and the final crack detection result. This system uses multiple processing stages including edge detection, proximity filtering, and line segment identification to accurately detect cracks while filtering out false positives, resolving the contradiction between automated speed and accurate detection
2Productivity
If automated crack detection is implemented, then productivity is improved, but false-positive rates increase
Solution Approach 1:
The patent segments the crack detection process into multiple distinct stages: edge detection to identify potential crack boundaries, proximity analysis to filter isolated edges, line segment identification to connect relevant edges, and crack assessment to evaluate the significance of detected features. This segmentation allows each stage to focus on specific aspects of crack detection, improving overall reliability while maintaining automated productivity
Solution Approach 2:
The patent implements feedback mechanisms where the proximity of edges to each other influences the detection and weighting of crack features. Edges that are close to other edges are identified as potential crack components, while isolated edges are filtered out as false positives. This feedback-based filtering significantly reduces false-positive rates while maintaining high inspection speed
3Device complexity
If simple edge detection is used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent divides the crack detection task into multiple processing stages: edge detection, proximity analysis, line segment identification, and crack assessment. Each stage builds upon the previous one, adding necessary complexity only where needed to improve measurement precision while keeping individual processing steps relatively simple and manageable
Data Source
AI summary
Systems and methods for detecting cracks in an electronic device are disclosed. In one embodiment, the method includes receiving an image of a front side of a mobile device and automatically identifying edges in the image. For given edges among the identified edges, the method includes determining whether another edge among the identified edges is present within a predetermined distance of the given edge. Next, straight line segments corresponding to the edges for which another edge is within the predetermined distance are identified, and then a crack evaluation assessment is assigned to the mobile device based at least in part on the identified straight line segments.