Vehicle Camera Misalignment Detection Using Body Edge Gradients
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Solution Overview
Problem
Existing camera misalignment detection methods for vehicles are computationally heavy, complex, and unreliable, often requiring additional markers or complex image processing, making them time-consuming and prone to errors.
Innovation Solution
A method utilizing a-priori knowledge of vehicle exterior edges to detect misalignment by checking for edge gradients at predetermined locations, using a simple edge detection and direction matching process, without requiring additional components or complex algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional markers or complex object-detection algorithms are used for misalignment detection, then detection reliability improves, but device complexity and computing power requirements increase
Solution Approach 1:
The camera system uses the vehicle's own body (bumper, door, etc.) as the reference object for misalignment detection, eliminating the need for additional external markers or complex detection algorithms. The vehicle structure itself serves as the calibration reference, simplifying the system while maintaining reliability
Solution Approach 2:
The system creates a reference image during calibration that captures the expected appearance of vehicle body parts. This reference image is then compared against subsequent images to detect misalignment, replacing complex real-time detection with a simpler comparison process
2Measurement precision
If additional markers or complex image processing algorithms are used, then detection precision improves, but computing power requirements and processing time increase
Solution Approach 1:
The reference image capturing and processing is performed in advance during calibration, storing the expected appearance of vehicle body parts. During operation, only simple comparison operations are needed, significantly reducing real-time computing power requirements while maintaining detection precision
Solution Approach 2:
The system extracts only the essential reference information (appearance of vehicle body parts) during calibration and separates it from the main detection process. This extraction allows the operational phase to use minimal computing resources for comparison-based detection
3Measurement precision
If additional markers or complex algorithms are implemented, then detection accuracy improves, but the system becomes more error-prone
Solution Approach 1:
By using the vehicle's own body as the reference instead of external markers, the system eliminates errors related to marker placement, visibility, and recognition. The vehicle body is always present and provides a stable, consistent reference that reduces detection errors
Solution Approach 2:
The vehicle body serves multiple functions: it is both the object being monitored and the reference standard for detection. This universal use of the vehicle structure as reference eliminates the need for separate reference objects, reducing potential sources of error
4Measurement precision
If complex object-detection algorithms are used, then detection capability improves, but processing speed decreases
Solution Approach 1:
Complex image processing operations are performed in advance during calibration to create the reference image. During operational misalignment detection, only simple image comparison is required, dramatically increasing processing speed while maintaining detection capability
Solution Approach 2:
Instead of using complex algorithms to detect misalignment directly from operational images, the system inverts the approach by comparing operational images against a pre-processed reference image. This inversion transforms a complex real-time detection problem into a simple comparison task
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables fast, reliable, and resource-efficient misalignment detection, allowing for timely online recalibration without additional sensors or complex processing, suitable for various vehicle parts and conditions.
Implementation Method 1
The camera has a field of view containing a vehicle image area imaging a portion of the vehicle and a remaining area imaging essentially surroundings of the vehicle
Data Source
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AI summary
The invention is a method for camera misalignment detection by means of processing images of a camera fixed to a vehicle and having a field of view containing a vehicle image area (11) imaging a portion of the vehicle and a remaining area (12), wherein in said processing step a plurality of reference locations (21) within an input image are used, said reference locations (21) being determined as edge locations along a boundary of the vehicle image area (11) in a reference image, and said processing step comprises checking at the reference locations (21) within the input image whether said reference locations (21) are edge locations by ascertaining at each reference location (21) whether a magnitude of an image gradient is above a gradient threshold limit, and alerting misalignment depending on an ascertained subset of the reference locations (21) where the gradient threshold limit is not reached. The invention also relates to a camera system, a computer program product and a computer-readable medium embodying the method.