Optical Usage Object Identification With Scaled Database Matching
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Solution Overview
Problem
Existing methods for identifying usage objects are inaccurate due to reliance on small optical and data-related details, leading to errors in comparison with database entries.
Innovation Solution
A method involving a processing unit that captures and compares characteristic values of usage objects with database entries, using a combination of optical and data-related analysis to ensure accurate and unique identification, potentially involving portable cameras and drones for efficient data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional photograph-based identification methods are used, then the system remains low in cost and simple to operate, but the identification accuracy deteriorates due to reliance on small optical and data-related details
Solution Approach 1:
The identification process is segmented into multiple independent detection steps (first physical detection, then optical detection) rather than relying on a single photograph-based method. This segmentation allows each detection method to focus on specific features, improving overall accuracy while maintaining manageable system complexity through modular processing.
Solution Approach 2:
A processing unit serves as an intermediary that coordinates between different detection methods (physical detection and optical detection). This intermediary integrates results from multiple sources, resolving the contradiction by systematically combining simple low-cost methods with more accurate but complex methods to achieve high accuracy without excessive overall complexity.
2Reliability
If multiple detection methods are combined to improve identification accuracy, then the reliability of object identification improves, but the device complexity and operational complexity increase
Solution Approach 1:
The system dynamically selects and sequences detection methods based on the identification process stage. Physical detection is performed first to obtain initial characteristics, followed by optical detection for verification and detailed analysis. This dynamic approach improves reliability by using multiple methods only when necessary, rather than continuously employing all detection capabilities.
Solution Approach 2:
Physical detection is performed as a preliminary action before optical detection. This preliminary step obtains basic characteristic values that can be used for initial identification or to guide subsequent optical detection, thereby improving overall reliability while reducing the burden on the more complex optical detection system.
3Measurement precision
If detailed optical and data-related analysis is performed to ensure accurate comparison, then the measurement precision improves, but the time required for detection and processing increases
Solution Approach 1:
The comparison process is segmented into physical characteristic comparison and optical characteristic comparison. Physical characteristics are compared first to quickly eliminate non-matching objects, then optical characteristics are analyzed in detail only for promising candidates. This segmentation maintains high comparison accuracy while reducing overall processing time by avoiding detailed optical analysis for all objects.
Solution Approach 2:
The system performs partial optical analysis on all objects (physical detection) and excessive/detailed optical analysis only on selected candidates. This approach ensures accurate comparison for final identification while minimizing time loss by limiting detailed analysis to a subset of objects that pass initial filtering.
4Ease of operation
If physical detection methods are used to obtain characteristic values, then the ease of operation is improved and cost is reduced, but the measurement precision and uniqueness of identification deteriorate
Solution Approach 1:
Physical detection and optical detection methods are merged into a unified identification process. Physical detection provides easy-to-obtain characteristic values for initial screening, while optical detection adds precision for final identification. This merging maintains operational simplicity by using straightforward physical methods first, then enhances precision by combining them with more accurate optical analysis.
Solution Approach 2:
The processing unit acts as an intermediary that bridges physical detection and optical detection. It takes simple physical characteristic values, processes them through optical detection for enhanced precision, and produces unique identification results. This intermediary approach maintains ease of operation by keeping the physical detection interface simple while achieving high identification precision through coordinated optical processing.
Data Source
AI summary
Disclosed is a method for the physical, in particular optical, detection of at least one usage object. The method includes the step of carrying out at least one physical detection process, for example by a user and/or an implementation device, in particular of at least one photograph, of the usage object, so that the usage object may be detected in such a way that an image of the usage object as detected during the detection process is shown at the same time as the database object shown on the screen in an identical manner or in a manner identical to scale, wherein as a result of live detection process, the usage object is associated with at least one usage object class, for example a vehicle type, by the processing unit and/or the CPU and/or the user.