Multi-Angle Vehicle Defect Measurement With Surface-Adaptive Correction
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Solution Overview
Problem
Detecting defects in vehicles' exterior surfaces, such as dents, bents, holes, tears, and scratches, is challenging due to extensive surfaces and the need to scan numerous vehicles efficiently, and existing methods struggle with accurate dimension estimation due to varying distances between vehicles and sensors.
Innovation Solution
Deploying image sensors paired with depth sensors to capture 2D and 3D images simultaneously, registering these images to determine the distance of defects, and using known reference features to compute real-world dimensions, allowing for efficient and accurate defect estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple image sensors are deployed to scan extensive vehicle surfaces, then defect detection coverage is improved, but system complexity and cost increase
Solution Approach 1:
The system divides the vehicle exterior scanning task into multiple segments by deploying image sensors at different locations (front, rear, sides) and processing defects in different vehicle regions separately. Each sensor captures a specific portion of the vehicle surface, and the system integrates these segmented measurements to achieve comprehensive coverage of the entire vehicle exterior.
2Measurement precision
If traditional single-distance measurement methods are used, then system simplicity is maintained, but measurement precision deteriorates due to varying distances
Solution Approach 1:
The system transitions from traditional 2D image analysis to 3D spatial measurement by integrating depth information. Depth sensors (such as time-of-flight sensors or stereo cameras) capture the third dimension (distance from sensor to vehicle surface), enabling accurate calculation of defect real-world dimensions even when the vehicle is at varying distances from the image sensors. This dimensional addition resolves the measurement accuracy problem without requiring fixed-distance positioning.
3Reliability
If comprehensive defect analysis is performed on all vehicle sides, then detection completeness is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and analyzing images from different vehicle sides in parallel during the vehicle scanning process. Instead of sequentially processing each side after complete data collection, the system begins analyzing defects on each vehicle side as soon as the corresponding images are captured, overlapping the data collection and processing phases to reduce total processing time while maintaining complete defect detection.
Data Source
AI summary
A method and system for estimating dimensions of vehicle exterior defects using multiple cameras arranged in a predefined configuration. The method comprises receiving images from multiple strategically positioned image sensors including side cameras parallel to a vehicle height axis, diagonal cameras at an inclined angle, and roof top cameras perpendicular to the height axis. An angle to detected defects is computed based on image sensor parameters. Different distance calculations are applied based on vehicle section location, with specialized formulas for windshield, back window, and roof components. Defect sizes are computed by determining multiple defect dimensions, with at least one dimension being adjusted by an angular correction factor derived from the relationship between camera angle and vehicle surface orientation. The system implements comprehensive validation through cross-referencing between cameras, comparison with known specifications, and measurement consistency analysis across multiple frames.