Lane Mark Verification Using Hysteresis and Speed Data
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Solution Overview
Problem
Existing lane mark detection methods struggle with mistaken recognition of objects other than lane marks, particularly in broken line configurations, due to the presence of blank regions and luminance changes from adjacent lanes, leading to unstable recognition performance.
Innovation Solution
A picture image processing apparatus and method that includes a candidate detection unit for identifying lateral and paint-blank boundary line candidates, a verification unit to confirm these candidates, and an estimation unit using hysteresis and car speed information to refine the positions of these boundaries, thereby reducing mistaken recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional white line detection methods are used to detect lane marks in broken line configuration, then detection speed is maintained, but mistaken recognition occurs around blank regions and luminance changes from adjacent lanes
Solution Approach 1:
The system performs preliminary actions by predicting the position of the lane mark in the current frame based on verification results from the previous frame. This prediction is done before actual detection, so when detection is performed, the system only needs to verify whether the detected position matches the predicted position, rather than detecting from scratch. This reduces mistaken recognition while maintaining processing efficiency.
Solution Approach 2:
The system implements feedback by using the verification result from the previous frame to inform the detection process in the current frame. The predicted position based on historical verification data serves as a reference for evaluating detection results, creating a closed-loop system that continuously refines recognition accuracy and reduces false detections.
2Ease of operation
If the detection method assumes lane mark is detected for all time, then processing is simplified, but objects other than lane marks are mistakenly detected due to blank regions and luminance changes
Solution Approach 1:
The system performs preliminary position prediction using verification results from the previous frame before conducting actual detection. This creates a reference framework that guides the detection process, making it simpler to evaluate whether detected features are genuine lane marks or false detections from adjacent lanes or blank regions.
Solution Approach 2:
The system dynamically adjusts the detection approach by switching between full detection and verification-based detection. When the vehicle is moving and previous verification data is available, the system uses dynamic position prediction and verification. When stationary or lacking historical data, it performs comprehensive detection. This dynamic adaptation maintains simplicity while improving reliability.
3Productivity
If verification is performed without using historical verification information, then processing speed is maintained, but mistaken recognition increases due to lack of contextual verification
Solution Approach 1:
The system performs preliminary position prediction using verification results from the previous frame before conducting actual detection. This prediction serves as a pre-established reference that accelerates the verification process in the current frame, allowing the system to quickly determine whether detected features match expected positions without performing exhaustive analysis.
Solution Approach 2:
The system implements feedback by continuously using verification results from previous frames to inform and constrain the verification process in the current frame. This creates a temporal feedback loop where historical accuracy information feeds into current detection, maintaining high processing speed while progressively improving recognition accuracy through accumulated contextual verification.
Data Source
AI summary
A picture image processing apparatus includes a candidate detection unit that detects a lateral boundary line candidate and a paint-blank boundary line candidate from the picture image information acquired, and a verification unit that verifies whether or not the lateral boundary line candidate etc. detected is the lateral boundary line etc.; a storage unit that memorizes, as hysteresis information, verification information including the lateral boundary line etc. in case the lateral boundary line candidate etc. detected has been verified to be a lateral boundary line etc. The storage unit also memorizes car speed information associated with the hysteresis information. The apparatus further includes an estimation unit that, using the hysteresis information and the car speed information, estimates positions of the lateral boundary line etc. in a next frame. The storage unit memorizes the estimation information including the position in the next frame of the lateral boundary line position etc. estimated. The verification unit uses the estimation information memorized at a time point of estimation in the storage unit to execute verification.