Lane Detection Device Dynamic Brightness Threshold Adjustment
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Solution Overview
Problem
Existing lane-dividing line detection devices face challenges in accurately detecting lane-dividing lines due to fixed brightness thresholds, leading to noise inclusion and missed detections, especially when lane paint is peeled or scraped, making it difficult to respond to varying road surface conditions.
Innovation Solution
A lane-dividing line detection device that dynamically adjusts the brightness threshold based on the number of candidate lines generated, increasing the threshold when noise is detected and decreasing it when lane lines are not detected, allowing for flexible extraction of characteristic points and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed brightness threshold is used to extract characteristic points, then the extraction process is simple and fast, but noise is easily included and false detection occurs
Solution Approach 1:
The patent applies the dynamics principle by transforming the fixed brightness threshold into a dynamic threshold that automatically adjusts based on the number of candidate lines detected. When the number of candidate lines exceeds the predetermined number, the threshold is increased to filter out noise; when it falls short, the threshold is decreased to ensure actual lane lines are detected. This dynamic adjustment resolves the contradiction by maintaining both extraction speed and detection accuracy under varying road conditions.
Solution Approach 2:
The patent applies the parameter changes principle by modifying the brightness threshold parameter based on the count of candidate lines. The threshold is not fixed but changes according to detection results: increased when too many candidates indicate noise, and decreased when too few candidates indicate missed detections. This parameter adaptation allows the system to maintain high detection accuracy while preserving processing efficiency.
2Reliability
If the brightness threshold is increased to reduce noise, then false detection decreases, but actual lane lines with peeled paint are not detected
Solution Approach 1:
The patent applies the feedback principle by using the number of detected candidate lines as feedback to adjust the brightness threshold. The system monitors whether the candidate line count exceeds or falls below the predetermined number and uses this information to adjust the threshold accordingly. This closed-loop feedback mechanism ensures that the threshold adapts to actual road conditions, reducing false detections while maintaining detection completeness even when lane paint is peeled or scraped.
3Measurement precision
If the brightness threshold is decreased to detect faded lines, then detection completeness improves, but noise inclusion increases
Solution Approach 1:
The patent applies the dynamics principle by making the brightness threshold adaptive rather than fixed. When the number of candidate lines falls below the predetermined number, indicating that actual lane lines are missed, the system dynamically decreases the threshold to improve detection completeness. Conversely, when too many candidates are detected, the threshold is increased to reduce noise. This dynamic behavior resolves the contradiction by adjusting the threshold according to actual detection needs.
Data Source
AI summary
The present invention relates to a lane-dividing line detection device and has an object of detecting a lane-dividing line with high accuracy by accurately extracting characteristic points of the lane-dividing line while responding flexibly to road surface conditions. According to the present invention, pixel parts where a brightness variation is larger than a predetermined threshold are extracted from an image picked up by a camera that picks up an area ahead of a vehicle as edge points representing the lane-dividing line on a road surface (step 106). Next, candidates for the lane-dividing line drawn on a road are generated based on the extracted edge points (step 108). Then, the predetermined threshold for the brightness variation used to extract the edge points is changed based on the number of the generated candidates for the lane-dividing line (steps 122 through 130). Specifically, if the number of the candidate lines exceeds a predetermined number, the threshold is changed to a high value so as to increase difficulty in extracting the edge points. On the other hand, if the number of the candidate lines does not reach the predetermined number, the threshold is changed to a low value so as to facilitate the extraction of the edge points.