Image Processor Road Marker Detection
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Solution Overview
Problem
Conventional image processing systems face challenges in accurately detecting parking area lines and lane boundaries due to shadows, reflections, and blurs, leading to false or missed detections, especially in areas with varying brightness and obstructions.
Innovation Solution
An image processor that connects broken markers on a road surface by detecting edges and using edge connection techniques to create a single marker, improving detection accuracy by filtering out noise and correcting endpoint positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional edge detection methods are used to detect parking area lines, then the detection process is simple, but the detection accuracy deteriorates due to shadows, reflections, and blurs causing false or missed detections
Solution Approach 1:
The patent segments the edge detection process into multiple stages: initial edge detection, candidate selection based on geometric constraints, and iterative refinement. This segmentation allows the system to handle complex conditions like shadows and reflections by processing the image in manageable steps rather than attempting a single-pass detection.
Solution Approach 2:
The patent performs preliminary actions by pre-defining candidate regions and constraints before final edge detection. It uses preliminary edge detection to identify potential parking area lines, then applies geometric constraints and iterative refinement to confirm accurate detection, thereby improving accuracy while managing complexity.
2Reliability
If the detection threshold is lowered to detect faint markers, then detection sensitivity improves, but false detections increase due to noise from shadows and reflections
Solution Approach 1:
The patent implements feedback mechanisms through iterative refinement where detected edges are evaluated against geometric constraints and physical properties of parking area lines. The system continuously adjusts detection parameters based on feedback from previous detection cycles, allowing it to distinguish true markers from noise caused by shadows and reflections while maintaining high reliability.
Solution Approach 2:
The patent dynamically changes detection parameters such as threshold values and search regions based on local image characteristics. By adapting parameters to specific regions (e.g., adjusting sensitivity in shadowed areas versus well-lit areas), the system maintains high detection reliability while minimizing false detections from noise interference.
3Measurement precision
If multiple processing steps are added to improve detection accuracy, then detection precision improves, but processing time increases
Solution Approach 1:
The patent applies partial action by performing full iterative refinement only for candidate regions that meet initial screening criteria. For clearly detectable markers, the system uses simplified processing paths, while for ambiguous cases, it applies more extensive processing steps. This approach maintains high detection precision while minimizing unnecessary processing time for straightforward cases.
Data Source
AI summary
An image processor includes an imaging device that captures an image of a road surface around a vehicle V, and a control portion that detects a marker drawn on the road surface from the captured image. The control portion connects a plurality of broken markers to create a single marker when the detected marker is broken into plural.