3D Scanner Vehicle Identification Number Recognition
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Solution Overview
Problem
Existing vehicle identification number recognition systems face errors due to contaminated surfaces, irregular reflections, varying light quantities, and inability to perceive depth, leading to incorrect recognition and the need for multiple standard patterns.
Innovation Solution
A system utilizing a 3D scanner to capture images of vehicle identification numbers, converting them to gray scale, extracting symbols, and calculating depth by analyzing height profiles, allowing for enhanced recognition rates and reduced standard patterns required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a vision system with lighting and camera is used to recognize vehicle identification number, then the system can capture images of the engraved surface, but recognition errors occur when the vehicle body surface is contaminated
Solution Approach 1:
The patent transitions from 2D optical imaging to 3D structured light scanning. By projecting multiple light patterns at different angles and capturing the deformed patterns, the system creates a three-dimensional representation of the engraved surface. This dimensional change allows the system to distinguish between surface contaminants (which do not create depth) and actual engraved characters (which do create depth), thereby resolving recognition errors caused by contamination.
Solution Approach 2:
The patent introduces structured light patterns as an intermediary between the light source and the camera. Instead of using direct illumination, the system projects known geometric patterns onto the surface and analyzes how these patterns deform when reflected from engraved areas. This intermediary approach enables the system to detect depth information and differentiate between contaminants and actual engravings, improving reliability.
2Measurement precision
If lighting position varies to illuminate the engraved surface, then the system can capture reflection patterns, but irregular reflection causes failure to correctly identify the vehicle identification number
Solution Approach 1:
The patent uses periodic action by projecting structured light patterns at multiple discrete angles in a systematic sequence. Instead of using continuous or random lighting variations, the system cycles through predetermined light projection angles, capturing images at each angle. This periodic approach allows the system to build a complete three-dimensional model of the engraved surface by combining data from multiple angular perspectives, ensuring consistent and accurate depth measurement regardless of single-angle irregularities.
Solution Approach 2:
The patent resolves lighting position variability by transitioning to 3D scanning with structured light. By capturing how light patterns deform at multiple angles and synthesizing this information into a three-dimensional depth map, the system eliminates the problems associated with 2D lighting variations. The multi-angle structured light approach creates a comprehensive depth representation that is invariant to single-angle reflection irregularities.
3Adaptability or versatility
If many standard patterns of symbols are used to compensate for varying light quantity, then the system can handle different lighting conditions, but the device complexity increases
Solution Approach 1:
The patent eliminates the need for multiple standard patterns by transitioning from 2D intensity-based recognition to 3D depth-based recognition. The structured light scanning system captures the geometric shape of engraved characters, which remains constant regardless of lighting conditions. This dimensional change to 3D space provides inherent adaptability to varying light quantities, as depth information is preserved even when illumination levels change, thereby reducing device complexity.
Solution Approach 2:
The patent replaces the complex system of multiple standard patterns with a simpler structured light scanning mechanism. Instead of maintaining extensive libraries of symbol variations to account for lighting changes, the system uses active structured light projection and 3D shape analysis. This substitution of the recognition approach from pattern-matching to geometric measurement simplifies the device while maintaining adaptability to different lighting conditions.
4Loss of information
If a camera takes a two-dimensional image, then the system can capture the vehicle identification number, but the system cannot perceive the engraved depth
Solution Approach 1:
The patent directly addresses the loss of depth information by transitioning from 2D camera imaging to 3D structured light scanning. The system projects structured light patterns and captures their deformation on the engraved surface, then uses triangulation and pattern matching algorithms to reconstruct the three-dimensional geometry of the engraved characters. This dimensional change to 3D space enables the system to perceive and measure engraved depth while maintaining practical device complexity through efficient computational methods.
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
The system enhances recognition accuracy by maintaining consistent light reflection and calculating engraved depth, enabling correct identification with fewer standard patterns and improved reliability.
Implementation Method 1
a camera for taking a photograph of the light reflected at the engraved surface
Data Source
AI summary
A system for recognizing a vehicle identification number includes a three dimensional scanner configured to scan, in a direction in which the vehicle identification number is engraved, a vehicle identification number engraved in a vehicle body to obtain an image. The system further includes an image processor configured to convert the image obtained by the three dimensional scanner into a gray image, divide the gray image according to a gray scale to extract a symbol in the image corresponding to a symbol engraved in the vehicle body, and compare the symbol with standard symbols to determine the vehicle identification number.